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WO2024083466A1 - Analyse automatisée d'images radiologiques - Google Patents

Analyse automatisée d'images radiologiques Download PDF

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Publication number
WO2024083466A1
WO2024083466A1 PCT/EP2023/076726 EP2023076726W WO2024083466A1 WO 2024083466 A1 WO2024083466 A1 WO 2024083466A1 EP 2023076726 W EP2023076726 W EP 2023076726W WO 2024083466 A1 WO2024083466 A1 WO 2024083466A1
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WIPO (PCT)
Prior art keywords
time series
image
radiological
area
examination
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PCT/EP2023/076726
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German (de)
English (en)
Inventor
Felix Karl KREIS
Gesine KNOBLOCH
Matthias LENGA
Ivo Matteo BALTRUSCHAT
Gregor Jost
Gunnar SCHÜTZ
Hubertus Pietsch
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Bayer Aktiengesellschaft
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Publication of WO2024083466A1 publication Critical patent/WO2024083466A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present disclosure relates to the analysis of radiological images.
  • Subject matters of the present invention include a computer-implemented method, a computer system and a computer program for segmenting radiological images and/or for identifying abnormalities in radiological images.
  • Medical imaging is the technique and process of imaging the interior of the body for clinical analysis and medical interventions, as well as visually representing the function of specific organs or tissues.
  • the purpose of medical imaging is to make internal structures hidden beneath the skin and bones visible and to diagnose and/or treat diseases. Advances in imaging and machine learning have led to a rapid increase in the potential use of artificial intelligence in various medical imaging tasks, such as risk assessment, detection, diagnosis, prognosis and therapy.
  • Machine learning models are used, among other things, to segment radiological images and/or to identify lesions and/or other abnormalities.
  • WO2021/038203A1 discloses a method for automatically segmenting structural features of blood vessels in radiological images using a trained machine learning model.
  • the machine learning model is trained using training data.
  • the training data includes a large number of non-segmented radiological images (as input data) and manually segmented radiological images (as target data).
  • the non-segmented images are presented to the model and the model generates segmented images (output data) based on the non-segmented images and model parameters.
  • the segmented images (output data) are compared with the target data.
  • WO2021/183765A1 discloses a method for identifying lesions using a machine learning model.
  • the model disclosed in WO2021/183765A1 is also trained using training data, the training data comprising a large number of manually labeled radiological images.
  • the labels indicate whether and where lesions can be seen in radiological images. Training such machine learning models requires a great deal of effort. On the one hand, a large number of radiological images must be provided, and on the other hand, they must be manually annotated (segmented and/or labeled).
  • a first subject matter of the present disclosure is a computer-implemented method comprising: - Receiving radiological images, the radiological images representing an examination area of at least one examination object at a number N of consecutive points in time, at least after an application of a contrast agent, each radiological image comprising a plurality of image elements, each image element representing a sub-area of the examination area, - for each image element: extracting one or more features from the radiological images, - generating a time series for each sub-area of the examination area, each time series of a sub-area comprising the one or more extracted features of the image elements representing the sub-area for the number N of consecutive points in time, - selecting at least some of the time series, - grouping the selected time series into a number M of groups, - assigning each non-selected time series to one of the M groups, - assigning each image element to the group to which the time series of the sub-area that the image element represents is assigned, - assigning a label from M labels to each image element, each
  • a further subject of the present disclosure is a computer system comprising an input unit, a control and computing unit and an output unit, wherein the control and computing unit is configured to - cause the input unit to receive radiological images, wherein the radiological images represent an examination area of at least one examination object at a number N of consecutive points in time at least after an application of a contrast agent, wherein each radiological image comprises a plurality of image elements, wherein each image element represents a sub-area of the examination area, - extract one or more features from the radiological images for each image element, - generate a time series for each sub-area of the examination area, wherein each time series of a sub-area comprises the one or more extracted features of the image elements representing the sub-area for the number N of consecutive points in time, - select at least some of the time series, - group the selected time series into a number M of groups, - assign each non-selected time series to one of the M groups, - assign each image element to the group to which the time series of that sub-area is assigned
  • the image element represents, - to assign one of M identifiers to each image element, each identifier of the M identifiers representing a group of the M groups, - to cause the output unit to output and/or store and/or transmit to a separate computer system at least one of the radiological images comprising the identifiers of the image elements of the radiological image.
  • a further subject of the present invention is a computer program product comprising a data storage device in which a computer program is stored that can be loaded into a working memory of a computer system and causes the computer system to carry out the following steps: - receiving radiological images, the radiological images representing an examination area of at least one examination object at a number N of consecutive points in time, at least after an application of a contrast agent, each radiological image comprising a plurality of image elements, each image element representing a sub-area of the examination area, - for each image element: extracting one or more features from the radiological images, - generating a time series for each sub-area of the examination area, each time series of a sub-area comprising the one or more extracted features of the image elements representing the sub-area for the number N of consecutive points in time, - selecting at least some of the time series, - grouping the selected time series into a number M of groups, - assigning each non-selected time series to one of the M groups, - assigning each image element to the group to
  • a further subject of the present disclosure is a use of a contrast agent in a radiological examination method, the radiological examination method comprising: - receiving radiological images, the radiological images representing an examination area of at least one examination object at a number N of consecutive times, at least after an application of the contrast agent, each radiological image comprising a plurality of image elements, each image element representing a sub-area of the examination area, - for each image element: extracting one or more features from the radiological images, - generating a time series for each sub-area of the examination area, each time series of a sub-area comprising the one or more extracted features of the image elements representing the sub-area for the number N of consecutive points in time, - selecting at least a portion of the time series, - grouping the selected time series into a number M of groups, - assigning each non-selected time series to one of the M groups, - assigning each image element to the group to which the time series of the sub-area that the image element represents is assigned, - assigning an
  • a further subject of the present disclosure is a contrast agent for use in a radiological examination method, wherein the radiological examination method comprises: - receiving radiological images, wherein the radiological images represent an examination area of at least one examination object at a number N of consecutive points in time, at least after an application of the contrast agent, wherein each radiological image comprises a plurality of image elements, wherein each image element represents a sub-area of the examination area, - for each image element: extracting one or more features from the radiological images, - generating a time series for each sub-area of the examination area, wherein each time series of a sub-area comprises the one or more extracted features of the image elements representing the sub-area for the number N of consecutive points in time, - selecting at least some of the time series, - grouping the selected time series into a number M of groups, - assigning each non-selected time series to one of the M groups, - assigning each image element to the group to which the time series of the sub-area that the image element represents is assigned,
  • a further subject of the present disclosure is a kit comprising - a contrast agent and - a computer program product comprising a data storage device in which a computer program is stored, which can be loaded into a working memory of a computer system. and causes the computer system to carry out the following steps: • Receiving radiological images, the radiological images representing an examination area of at least one examination object at a number N of consecutive points in time, at least after an application of the contrast agent, each radiological image comprising a plurality of image elements, each image element representing a sub-area of the examination area, • for each image element: extracting one or more features from the radiological images, • generating a time series for each sub-area of the examination area, each time series of a sub-area comprising the one or more extracted features of the image elements representing the sub-area for the number N of consecutive points in time, • selecting at least some of the time series, • grouping the selected time series into a number M of groups, • assigning each non-selected time series to one of the M groups, •
  • the “examination object” is usually a living being, preferably a mammal, very particularly preferably a human.
  • the “examination area” is a part of the examination object, for example an organ or part of an organ such as the liver, brain, heart, kidney, lung, stomach, intestine or part of the organs mentioned or several organs or another part of the body.
  • the examination area comprises a liver or part of a liver or the examination area is a liver or part of a liver of a mammal, preferably a human.
  • the examination area comprises a brain or part of a brain or the examination area is a brain or part of a brain of a mammal, preferably a human.
  • the examination area comprises a heart or part of a heart or the examination area is a heart or part of a heart of a mammal, preferably a human.
  • the examination area comprises a thorax or part of a thorax or the examination area is a thorax or part of a thorax of a mammal, preferably a human.
  • the examination area comprises a stomach or part of a stomach or the examination area is a stomach or part of a stomach of a mammal, preferably a human.
  • the examination area comprises a pancreas or part of a pancreas or the examination area is a pancreas or part of a pancreas of a mammal, preferably a human.
  • the examination area comprises a kidney or part of a kidney or the examination area is a kidney or part of a kidney of a mammal, preferably a human.
  • the examination area comprises one or both lungs or part of a lung of a mammal, preferably a human.
  • the examination area comprises a breast or part of a breast, or the examination area is a breast or part of a breast of a female mammal, preferably a female human.
  • the examination area comprises a prostate or part of a prostate, or the examination area is a prostate or part of a prostate of a male mammal, preferably a male human.
  • the examination area also called field of view (FOV), represents in particular a volume that is depicted in radiological images.
  • the examination area is typically determined by a radiologist, for example on an overview image (localizer).
  • the examination area can alternatively or additionally, automatically, for example on the basis of a selected protocol.
  • a radiological image is preferably the result of a radiological examination.
  • “Radiology” is the branch of medicine that deals with the use of predominantly electromagnetic radiation and (including ultrasound diagnostics, for example) mechanical waves for diagnostic, therapeutic and/or scientific purposes.
  • radiological examination is a magnetic resonance imaging examination.
  • the radiological examination is an MRI examination in which an MRI contrast agent is used.
  • the radiological examination is a CT examination in which a CT contrast agent is used.
  • the radiological examination is a CT examination in which an MRI contrast agent is used.
  • Magnetic resonance imaging abbreviated to MRI or MRI, is an imaging method that is used primarily in medical diagnostics to depict the structure and function of tissues and organs in the human or animal body.
  • MR imaging the magnetic moments of protons in an object under examination are aligned in a basic magnetic field so that a macroscopic magnetization is established along a longitudinal direction. This is then deflected from the rest position by the radiation of high-frequency (HF) pulses (excitation). The return of the excited states to the rest position (relaxation) or the magnetization dynamics is then detected as relaxation signals using one or more RF receiving coils.
  • HF high-frequency
  • a radiological image in the sense of the present disclosure can be an MRI image, a computer tomogram, an ultrasound image or the like.
  • a radiological image in the sense of the present disclosure is preferably a representation of an examination region of an examination object in the spatial space (image space). In a representation in spatial space, the examination area is usually represented by a large number of image elements (e.g.
  • each image element is usually assigned a color value or gray value.
  • DICOM Digital Imaging and Communications in Medicine
  • the color value or gray value that can be assigned to each image element usually represents an intensity of a signal that is measured in the respective radiological imaging procedure. Different types of tissue in the examination area usually produce different signal intensities that are made visible in the radiological image.
  • Contrast agents can be used to enhance contrasts between different types of tissue in radiological images.
  • "Contrast agents” are substances or mixtures of substances that improve the representation of structures and functions of the body in radiological imaging procedures. Examples of contrast agents can be found in the literature (see e.g. A. S. L. Jascinth et al.: Contrast Agents in computed tomography: A Review, Journal of Applied Dental and Medical Sciences, 2016, Vol.2, Issue 2, 143 – 149; H. Lusic et al.: X-ray-Computed Tomography Contrast Agents, Chem. Rev.
  • contrast agents In computed tomography, iodine-containing solutions are usually used as contrast agents.
  • MRI magnetic resonance imaging
  • superparamagnetic substances e.g. iron oxide nanoparticles, superparamagnetic iron-platinum particles (SIPPs)
  • paramagnetic substances e.g. gadolinium chelates, manganese chelates, hafnium chelates
  • contrast agents In the case of sonography, fluids containing gas-filled microbubbles are usually administered intravenously. Examples of contrast agents can be found in the literature (see e.g. A. S. L.
  • MRI contrast agents exert their effect in an MRI examination by changing the relaxation times of the structures that absorb contrast agents.
  • Two groups of substances can be distinguished: para- and superparamagnetic substances. Both groups of substances have unpaired electrons that induce a magnetic field around the individual atoms or molecules.
  • the effect of these contrast agents is indirect, since the contrast agent itself does not emit a signal, but only influences the signal intensity in its surroundings.
  • An example of a superparamagnetic contrast agent is iron oxide nanoparticles (SPIO).
  • paramagnetic contrast agents examples include gadolinium chelates such as gadopentetate dimeglumine (trade name: Magnevist ® etc.), gadoteric acid (Dotarem ® , Dotagi ® , Cyclolux ® ), gadodiamide (Omniscan ® ), gadoteridol (ProHance ® ), Gadobutrol (Gadovist ® ), gadopiclenol (Elucirem, Vueway) and gadoxetic acid (Primovist ® /Eovist ® ).
  • gadolinium chelates such as gadopentetate dimeglumine (trade name: Magnevist ® etc.), gadoteric acid (Dotarem ® , Dotagi ® , Cyclolux ® ), gadodiamide (Omniscan ® ), gadoteridol (ProHance ® ), Gadobutrol (Gadov
  • the contrast agent is an agent comprising gadolinium(III) 2-[4,7,10-tris(carboxymethyl)-1,4,7,10-tetrazacyclododec-1-yl]acetic acid (also referred to as gadolinium-DOTA or gadoteric acid).
  • the contrast agent is an agent comprising gadolinium(III) ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA); preferably, the contrast agent comprises the disodium salt of gadolinium(III)-ethoxybenzyl-diethylenetriaminepentaacetic acid (also referred to as gadoxetic acid).
  • the contrast agent is an agent comprising gadolinium(III) 2-[3,9-bis[1-carboxylato-4-(2,3-dihydroxypropylamino)-4-oxobutyl]-3,6,9,15-tetrazabicyclo[9.3.1]pentadeca-1(15),11,13-trien-6-yl]-5-(2,3-dihydroxypropylamino)-5-oxopentanoate (also referred to as gadopiclenol) (see, e.g., WO2007/042504 and WO2020/030618 and/or WO2022/013454).
  • the contrast agent is an agent comprising dihydrogen[( ⁇ )-4-carboxy-5,8,11-tris(carboxymethyl)-1-phenyl-2-oxa-5,8,11-triazatridecane-13-oato(5-)]gadolinate(2-) (also referred to as gadobenic acid).
  • the contrast agent is an agent comprising tetragadolinium [4,10-bis(carboxylatomethyl)-7- ⁇ 3,6,12,15-tetraoxo-16-[4,7,10-tris-(carboxylatomethyl)-1,4,7,10-tetraazacyclododecan-1-yl]-9,9-bis( ⁇ [( ⁇ 2-[4,7,10-tris-(carboxylatomethyl)-1,4,7,10-tetraazacyclododecan-1-yl]propanoyl ⁇ amino)acetyl]-amino ⁇ methyl)- 4,7,11,14-tetraazahepta-decan-2-yl ⁇ -1,4,7,10-tetraazacyclododecan-1-yl]acetate (also referred to as gadoquatrane) (see, e.g., J.
  • the contrast agent is an agent that contains a Gd 3+ -Complex of a compound of formula (I) (I) , wherein Ar is a group selected from where # represents the connection to X, X represents a group consisting of CH 2 , (CH 2 ) 2 , (CH 2 ) 3 , (CH 2 ) 4 and *-(CH 2 ) 2 -O-CH 2 - # is selected, where * represents the connection to Ar and # represents the link to the acetic acid residue, R 1 , R 2 and R 3 independently a hydrogen atom or a group selected from C 1 -C 3 - Alkyl, -CH 2 OH, -(CH 2 ) 2 OH and -CH 2 OCH 3 represent, R 4 a group selected from C 2 -C 4 -Alkoxy, (H 3 C-CH 2 )-O-(CH 2 ) 2 -O-, (H 3 C-CH 2 )-O-(CH 2 ) 2
  • the contrast agent is an agent comprising a Gd 3+ -Complex of a compound of formula (II) (II) , wherein Ar is a group selected from R where # represents the link to X, X represents a group consisting of CH 2 , (CH 2 ) 2 , (CH 2 ) 3 , (CH 2 ) 4 and *-(CH 2 ) 2 -O-CH 2 - # is selected, where * represents the connection to Ar and # represents the link to the acetic acid residue, R 7 a hydrogen atom or a group selected from C 1 -C 3 -alkyl, -CH 2 OH, -(CH2)2OH and -CH2OCH3; R 8th a group selected from C 2 -C 4 -Alkoxy, (H 3 C-CH 2 O)-(CH 2 ) 2 -O-, (H 3 C-CH 2 O)-(CH 2 ) 2 -O-(CH 2 ) 2 -
  • C 1 -C 3 -Alkyl means a linear or branched, saturated, monovalent hydrocarbon group having 1, 2 or 3 carbon atoms, e.g. methyl, ethyl, n-propyl and isopropyl.
  • C 2 -C 4 -Alkyl means a linear or branched, saturated, monovalent hydrocarbon group having 2, 3 or 4 carbon atoms.
  • C 2 -C 4 -Alkoxy means a linear or branched, saturated, monovalent group of the formula (C 2 -C 4 -alkyl)-O-, in which the term "C 2 -C 4 -Alkyl” is as defined above, e.g.
  • the contrast agent is an agent comprising gadolinium 2,2',2''-(10- ⁇ 1-carboxy-2-[2-(4-ethoxyphenyl)ethoxy]ethyl ⁇ -1,4,7,10-tetraazacyclododecane-1,4,7-triyl)triacetate (see e.g. WO2022/194777, Example 1).
  • the contrast agent is an agent comprising gadolinium 2,2',2''- ⁇ 10-[1-carboxy-2- ⁇ 4-[2-(2-ethoxyethoxy)ethoxy]phenyl ⁇ ethyl]- 1,4,7,10-tetraazacyclododecane-1,4,7-triyl ⁇ triacetate (see e.g. WO2022/194777, Example 2).
  • the contrast agent is an agent comprising gadolinium 2,2',2''- ⁇ 10-[(1R)-1-carboxy-2- ⁇ 4-[2-(2-ethoxyethoxy)ethoxy]phenyl ⁇ ethyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl ⁇ triacetate (see e.g. WO2022/194777, Example 4).
  • the contrast agent is an agent comprising gadolinium (2S,2'S,2''S)-2,2',2''- ⁇ 10-[(1S)-1-carboxy-4- ⁇ 4-[2-(2-ethoxyethoxy)ethoxy]phenyl ⁇ butyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl ⁇ tris(3-hydroxypropanoate) (see e.g. WO2022/194777, Example 15).
  • the contrast agent is an agent comprising gadolinium 2,2',2''- ⁇ 10-[(1S)-4-(4-butoxyphenyl)-1-carboxybutyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl ⁇ triacetate (see e.g. WO2022/194777, Example 31).
  • the contrast agent is an agent comprising gadolinium 2,2',2''- ⁇ (2S)-10-(carboxymethyl)-2-[4-(2-ethoxyethoxy)benzyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl ⁇ triacetate.
  • the contrast agent is an agent comprising gadolinium 2,2',2''-[10-(carboxymethyl)-2-(4-ethoxybenzyl)-1,4,7,10-tetraazacyclododecane-1,4,7-triyl]triacetate.
  • the contrast agent is an agent comprising gadolinium(III) 5,8-bis(carboxylatomethyl)-2-[2-(methylamino)-2-oxoethyl]-10-oxo-2,5,8,11-tetraazadodecane-1-carboxylate hydrate (also referred to as gadodiamide).
  • the contrast agent is an agent comprising gadolinium(III) 2-[4-(2-hydroxypropyl)-7,10-bis(2-oxido-2-oxoethyl)-1,4,7,10-tetrazacyclododec-1-yl]acetate (also referred to as gadoteridol).
  • the contrast agent is an agent comprising gadolinium(III) 2,2',2''-(10-((2R,3S)-1,3,4-trihydroxybutan-2-yl)-1,4,7,10-tetraazacyclododecane-1,4,7-triyl)triacetate (also referred to as gadobutrol or Gd-DO3A-butrol).
  • gadolinium(III) 2,2',2''-(10-((2R,3S)-1,3,4-trihydroxybutan-2-yl)-1,4,7,10-tetraazacyclododecane-1,4,7-triyl)triacetate also referred to as gadobutrol or Gd-DO3A-butrol.
  • segmentation refers to the process of dividing a radiological image into several segments, which are also called image segments, image regions or image objects. Segmentation is usually performed to localize objects and boundaries (lines, curves, planes, surfaces, etc.) in radiological images. From a segmented radiological image, the localized objects can be separated from the background, visually highlighted (e.g.: colored), measured, counted or otherwise quantified. During segmentation, each image element (pixel/voxel/doxel/n-xel) of a radiological image is assigned an identifier so that image elements with the same identifier have certain features in common. In a first step of the segmentation process, radiological images are received.
  • the term “receiving” includes both the retrieval of radiological images and the receipt of radiological images that are transmitted, for example, to the computer system of the present disclosure.
  • the radiological images can be received from a computer tomograph, a magnetic resonance tomograph or an ultrasound scanner.
  • the radiological images can be read from one or more data storage devices and/or transmitted from a separate computer system.
  • the radiological images represent an examination area of one or more examination objects.
  • the examination area is usually the same for all examination objects (e.g. the liver).
  • the radiological images represent the examination area at different times, at least after the application of a contrast agent. In one embodiment of the present disclosure, the radiological images represent the examination area at different times before and after the application of a contrast agent.
  • the radiological images form a sequence in which the dynamic behavior of the contrast agent in the examination area is shown.
  • the times form a series of consecutive times: t 1 , t 2 , ..., t N
  • the term “consecutive” means that there is a first point in time t 1 and a second time t 2 where the second point in time is later than the first point in time t 1
  • There may be further points in time for example a third point in time t 3 .
  • the third point in time is then later than the first point in time t 1 and the second time t 2 .
  • the term "successive" does not mean that the points in time immediately follow one another, i.e. it does not mean that there may be no period of time between the points in time.
  • at least one point in time occurs before an application of a contrast agent, while preferably at least two points in time occur after the application of the contrast agent.
  • the radiological images preferably comprise at least one radiological image that represents an examination area of an examination object before the application of a contrast agent (i.e. without contrast agent) and at least two radiological images that represent the examination area of the examination object after the application of the contrast agent. It is possible for a contrast agent to be applied multiple times, i.e. for there to be more than one application of a contrast agent.
  • the number N of points in time t 1 are N is preferably at least 3.
  • the number N of points in time is particularly preferably in the range of 5 to 20, but is not limited to 20 points in time.
  • the points in time can have a constant distance or a varying distance from one another. If radiological images of the examination area are received from several (different) examination objects, the points in time at which they were generated are preferably the same.
  • Each radiological image comprises a large number of image elements (pixels/voxels/doxels, n-xels).
  • the number of image elements can also be larger or smaller.
  • Each image element represents a sub-area of the examination area. It is possible to co-register the various radiological images.
  • the “co-registration” also called “image registration” in the state of the art) serves to bring two or more radiological images of the same examination area into optimal agreement with one another.
  • One of the radiological images is defined as the reference image, and the other radiological images are called object images. In order to optimally adapt the object images to the reference image, a compensating transformation is calculated.
  • radiological images For each image element, one feature or several features are extracted from the radiological images.
  • An extracted feature can be a radiomics feature, such as a signal intensity and/or a gradient (e.g. after executing a Sobel and/or Kirsch gradient operator).
  • the term “radiomics” refers to the quantitative extraction of, for example, histogram and/or texture features from radiological images (see, for example, M. E. Mayerhoefer et al: Introduction to Radiomics, The Journal of Nuclear Medicine, 2020, Vol.61, No.4, pages 488-495; C.
  • An extracted feature can, for example, be an intensity value of a signal (signal intensity).
  • the intensity value can, for example, be the intensity of X-rays after they have passed through the examination area.
  • the intensity value can, for example, be the intensity of a T1-weighted signal or a T2-weighted signal or another intensity value of an MRI measurement sequence.
  • the intensity value can be, for example, the gray value (in the case of a gray value representation) or the color value (in the case of a color representation) of the respective image element of the respective radiological image.
  • a time series is generated on the basis of the received radiological images. Each time series comprises the characteristics of those image elements that represent the examination area for the number N of consecutive points in time.
  • each time series is formed for a sub-area on the basis of the image elements that represent the sub-area in the radiological images; each image element represents the sub-area at a different point in time; the characteristics of the image elements as a function of the consecutive points in time form a time series.
  • each time series has exactly one value of a feature (e.g. intensity value of a signal) for each point in time of the consecutive points in time.
  • a temporal standardization is carried out.
  • the time of application of a contrast agent is preferably the time at which the application is complete, i.e. the intended amount of contrast agent, for example in the form of a bolus, has been completely introduced into a part of the body of the object being examined (e.g. into the arm vein in a person).
  • the time at which a radiological image is created is the time at which the creation is complete.
  • the missing values in the time series can be supplemented, for example, by interpolation and/or extrapolation. It is also conceivable that the temporal progression of the signal intensities in a dynamic contrast-enhanced radiological examination for different examination objects varies due to different anatomy and/or physiology. It is therefore possible that the time series of different examination objects are standardized with regard to the intensity values and the time points. An example of standardization is described further down in the description. It is also conceivable to take further steps to create the values for the time series, e.g.
  • the time series values created in this way can then be standardized, as is usual in time series analysis, so that they all have the same variance and the same mean, for example. At least part of the time series is selected in order to group the selected time series.
  • clustering refers to methods for identifying similarity structures in data and grouping similar objects into groups, which are also called clusters. Such methods are also known as cluster analysis.
  • the aim of grouping is to identify new groups in the data (in contrast to classification, in which data is assigned to existing classes).
  • the selected part can be, for example, 1%, 5%, 10%, 15%, 20%, 30%, 50%, 80%, 90% or another percentage.
  • the selected part can also be 100%, i.e. all time series can be selected and grouped.
  • a part that is smaller than 100% is preferably selected.
  • the remaining, non-selected time series can be assigned to the groups formed in a classification process that is less computationally intensive.
  • the selected part should therefore be representative of the set of available time series, i.e.
  • all sub-areas that show a characteristic temporal behavior after application of a contrast agent should be represented.
  • the selection can be made randomly. However, it is also possible to specifically select time series from defined sub-areas of the examination area. For example, it is possible for a radiologist to specify sub-areas in one or more radiological images (for example using an input device such as a computer mouse) whose time series should be included in the grouping, for example because they show different types of tissue in one or more radiological images and/or the radiologist knows from experience that the sub-areas show a characteristic signal curve in the dynamic contrast-enhanced radiological examination.
  • the selected time series are grouped into a number M of groups. During grouping, each time series is assigned to exactly one of the M groups.
  • Time-series clustering There are a variety of methods for time-series clustering (see e.g.: E. ⁇ zkoç, Esma (2020), Clustering of Time-Series Data, 10.5772/intechopen.84490; A. Sardá-Espinosa: Comparing Time-Series Clustering Algorithms in R Using the dtwclust Package, The R Journal Vol. 11/01, June 2019 ISSN 2073-4859; S. Aghabozorgi et al.: Time-series clustering - A decade review, Information Systems, 2015, 53, 16-38).
  • Time series clustering is usually based on determining a similarity measure or a distance measure. A similarity measure usually indicates how similar two time series are to each other.
  • Minkowksi distance corresponds to the Manhattan distance
  • Minkowksi distance corresponds to the Euclidean distance.
  • ⁇ ( ⁇ , ⁇ ) m ⁇ i ⁇ n ⁇
  • ⁇ is a path that aligns the time series such that the Euclidean distance between aligned (i.e. resampled) time series is minimal.
  • Other examples of distance measures are Short Time-Series Distance (STS) and DISSIM distance.
  • STS Short Time-Series Distance
  • DISSIM distance is a distance measure that measures the difference or similarity of time series based on intensity values.
  • features for example in the form of feature vectors
  • similarity measures/distance measures based on extracted features are Pearson’s correlation coefficient and periodogram-based distance.
  • time series and/or radiological images
  • transformations are Fourier transform and wavelet transform.
  • distance measures and similarity measures described here, as well as others that can be used, are described in numerous publications on the topic of time-series clustering (see e.g.: E. ⁇ zkoç, Esma (2020), Clustering of Time-Series Data, 10.5772/intechopen.84490; A. Sardá-Espinosa: Comparing Time-Series Clustering Algorithms in R Using the dtwclust Package, The R Journal Vol.11/01, June 2019 ISSN 2073-4859; S.
  • time series X and Y described above can be understood as vectors in an N-dimensional space. Each time series defines a point in the N-dimensional space through its vector (starting from the origin of the N-dimensional space). Usually there are not just two but a large number of such vectors/points in the N-dimensional space (as many as time series have been selected).
  • a center of gravity can be calculated for a group (a cluster) of points.
  • the center of gravity can, for example, be the point in the N-dimensional space that is defined by a vector whose elements are formed by the arithmetically averaged mean values of the elements of all vectors belonging to the cluster.
  • centroid vector (x s1 , x s2 , ..., x sn )
  • the calculation can be done by element-wise arithmetic averaging, i.e., the first element x s1 can be calculated by the arithmetic mean of the first elements of the vectors X j formed, the second element x s2 by the arithmetic mean of the second elements of the vectors X j and so on.
  • the point specified by the centroid vector starting from the zero point of the N-dimensional space is the centroid of the group (cluster).
  • a centroid of a group can be considered as the center of the group (group center/cluster center).
  • An example of an iterative algorithm for grouping the time series is: 1.
  • a number M of cluster centers are randomly created in the N-dimensional space. 2. Each time series is assigned to the cluster center from which it has a minimum distance d. 3. For each cluster, the cluster center is recalculated as the centroid of the time series forming the cluster. 4. Steps 2 and 3 are repeated as often as the assignment of the time series to the clusters changes.
  • minimal distance means that there is no cluster center for a time series that is closer to the time series than the cluster center that is the closest to it.
  • the number M of groups into which the time series are grouped can be determined by a radiologist. Preferably, the number M of groups corresponds to the number of sub-areas of the examination area that show different/characteristic dynamic behavior after application of a contrast agent.
  • a hepatobiliary contrast agent is characterized by the fact that it is specifically absorbed by liver cells, the hepatocytes, accumulates in the functional tissue (parenchyma) and increases the contrast in healthy liver tissue.
  • An example of a hepatobiliary contrast agent is the disodium salt of gadoxetic acid (Gd-EOB-DTPA disodium), which is described in US Patent No.6,039,931A under the brand name Primovist ® and Eovist ® is commercially available.
  • hepatobiliary contrast agents are described in WO2022/194777, among others.
  • Another MRI contrast agent with lower uptake into the hepatocytes is gadobenate dimeglumine (Multihance®).
  • Multihance® gadobenate dimeglumine
  • the contrast agent After the intravenous administration of a hepatobiliary contrast agent in the form of a bolus into a vein in a person's arm, the contrast agent initially reaches the liver via the arteries. These are shown with contrast enhancement in the corresponding MRI images.
  • the phase in which the liver arteries are shown with contrast enhancement in MRI images is called the "arterial phase".
  • the contrast agent then reaches the liver via the liver veins. While the contrast in the liver arteries is already decreasing, the contrast in the liver veins reaches a maximum.
  • the phase in which the liver veins are shown with contrast enhancement in MRI images is called the "portal venous phase".
  • the portal venous phase is followed by the “transitional phase,” in which the contrast in the liver arteries continues to decrease, and the contrast in the liver veins also decreases.
  • the contrast in the healthy liver cells gradually increases in the transitional phase. 10-20 minutes after its injection, a hepatobiliary contrast agent leads to a significant signal enhancement in the healthy liver parenchyma. This phase is referred to as the “hepatobiliary phase.”
  • the contrast agent is only slowly excreted from the liver cells; accordingly, the hepatobiliary phase can last two hours or more.
  • Fig. 1 shows the course of the signal intensities in a magnetic resonance imaging examination for three different sub-areas of an examination area that includes the liver of a person as a function of time t.
  • the T1-weighted signal intensities are plotted on the ordinate as intensity values I.
  • the graph marked with A shows the temporal course of the signal intensity in a hepatic artery.
  • the graph marked with The graph marked V shows the temporal progression of the signal intensity in a liver vein.
  • the graph marked L shows the temporal progression of the signal intensity in healthy liver cells. All three signal curves are characteristic and can be easily distinguished from one another. The signal curves are similar in all healthy people. In order to group signal curves (time series) from different examination objects (people), the signal curves of the different examination objects can be normalized.
  • the time of application of the contrast agent can be set to time 0 for all examination objects.
  • the characteristic signal curve in the liver artery of an examination object can be used as a reference.
  • the signal curves in the liver artery of the other examination objects can be adapted to the reference signal curve by means of a transformation (e.g. stretching, compression) so that they are aligned as best as possible.
  • the transformation can then be applied to all other signal curves (time series) of all examination objects.
  • the radiological images include the liver and the gallbladder, an accumulation of contrast medium in the gallbladder can often be observed.
  • a number of 3 to 5 groups can be determined. If only the liver is captured, the arteries, veins and healthy liver cells differ in their dynamic appearance during the contrast-enhanced MRI examination (see Fig.
  • a grouping into three groups can be made. If the gallbladder is captured in addition to the liver, this differs from the sub-areas already mentioned; a grouping into four groups can be made. If, in addition to the liver and gallbladder, sub-areas are captured to which the contrast medium does not reach or only reaches a comparatively small amount, a grouping into five groups can be made. Alternatively, mathematical heuristics can also be used to determine the number M of groups, for example the elbow method or the silhouette coefficient method. If the selected time series are grouped, M groups have been formed and each selected time series is assigned to one of the M groups. In a further step, the remaining, non-selected time series are assigned to the groups already formed.
  • Each non-selected time series is assigned to exactly one group. This process is also known as classification. Classification can be carried out, for example, by calculating the distance (the distance measure) to the group center for each non-selected time series. Each non-selected time series is assigned to the group from whose group center it has a minimum distance. If all time series are assigned to a group of the M groups, the group membership can be transferred to the corresponding image elements that belong to the time series. Each time series has a number N of image elements. These N image elements can be assigned to the group to which the time series is assigned. If all image elements are assigned, the image elements can be labeled according to their group membership. Each image element is assigned an identifier, whereby the identifier represents the group to which the image element is assigned.
  • each image element is assigned one of the M identifiers.
  • All image elements of a radiological image with the same identifier can form a segment in the radiological image.
  • all image elements representing hepatic arteries can be assigned to a first group
  • all image elements representing hepatic veins be assigned to a second group
  • all image elements that represent healthy liver cells are assigned to a third group.
  • the identifier that is/will be assigned to each group can be a number, for example.
  • the identifier "0" can mean, for example, that the image element in a radiological image should be displayed in a red color tone.
  • the identifier "1” can mean, for example, that the image element in a radiological image should be displayed in a green color tone.
  • the identifier "2" can mean, for example, that the image element in a radiological image should be displayed in a blue color tone.
  • the radiological images are in a format where hues are defined according to the RGB color space with 256 hues (RGB stands for the primary colors red, green and blue), the labels can also indicate the corresponding hues: (255, 0, 0), (0, 255, 0), (0, 0, 255).
  • RGB stands for the primary colors red, green and blue
  • the labels can also indicate the corresponding hues: (255, 0, 0), (0, 255, 0), (0, 0, 255).
  • these examples are for illustration purposes only.
  • the color saturation, hue or transparency of the label changes as a function of the distance measure, so that the label becomes more noticeable to the human eye the greater the deviation of the underlying time series from the cluster centroid.
  • the radiological images comprising the characteristics for the image elements can be output (e.g. displayed on a screen or printed out on a printer), stored (e.g.
  • the user e.g. a radiologist
  • the number M of groups For example, he can specify how many groups the time series should be divided into by entering data into the computer system of the present disclosure, or, as explained above, use a mathematical heuristic to help determine the number M.
  • the user can, for example, try out different numbers of groups and have the segmented radiological images output (e.g. displayed on a screen) to check which number M produces a better division of the radiological images into segments.
  • sequences of radiological images that were not included in the grouping can also be segmented.
  • a sequence of radiological images of a different (new) examination object can be segmented.
  • a sequence of an examination object from which radiological images have been included in a grouping i.e. have been used for the grouping
  • the process for segmenting new radiological images can, for example, comprise: • Receiving a new sequence of radiological images, wherein the radiological images of the new sequence represent an examination region of an examination object at a number N of consecutive points in time at least after an application of a contrast agent, wherein each radiological image comprises a plurality of image elements, wherein each image element represents a sub-area of the examination region, • for each image element: extracting one or more features from the radiological images of the new sequence of radiological images, - generating a time series for each sub-area of the examination area based on the radiological images of the new sequence, each time series of a sub-area comprising the one or more extracted features of the image elements representing the sub-area for the number N of consecutive points in time, • assigning each time series to one of the M groups, • assigning the image elements of each time series to the group to which the time series is assigned, • assigning one of the M characteristics to each image element, each characteristic of the M characteristics representing a group of the M groups, • outputting
  • groups can be formed on the basis of healthy examination objects and a group assignment can be checked on the basis of diseased examination objects. If one or more sub-areas cannot be clearly assigned to an existing group during group assignment (classification), this is an abnormality that may indicate an illness.
  • changes in radiological images of an examination object can be detected.
  • groups are formed on the basis of radiological images of an examination object, whereby the radiological images represent an examination area of the examination object in a first time period. Radiological images of the examination area of the same examination object obtained in a later second time period can be assigned to the groups formed in a second step.
  • a grouping is carried out based on radiological images that show one or more abnormalities.
  • One or more groups represent sub-areas that show one or more abnormalities.
  • a group assignment is carried out based on radiological images of another object under examination.
  • radiological images are received from at least one healthy object under examination.
  • Healthy means that no signs of illness can be seen in the radiological images.
  • the radiological images represent an examination area of the at least one healthy examination object at a number N of consecutive points in time at least after an application of a contrast agent.
  • Each radiological image comprises a large number of image elements.
  • Each image element represents a sub-area of the examination area. For each image element of at least some of the sub-areas, one or more features are extracted from the radiological images.
  • time series are generated on the basis of the radiological images.
  • Each time series comprises values of the one or more features for the number N of consecutive points in time.
  • the time series are grouped in a cluster analysis into a number M of groups, as described in this description. This means that M groups are available that can be used for classification.
  • radiological images are received from another examination object.
  • the radiological images preferably represent the examination area of the further examination object at the number N of consecutive points in time at least after the application of a contrast agent.
  • the contrast agent is preferably the same as in the case of the at least one healthy examination object.
  • Each radiological image comprises a plurality of image elements.
  • Each image element represents a sub-area of the examination area.
  • one or more features are extracted from the radiological images.
  • time series are generated on the basis of the radiological images.
  • Each time series comprises values of the one or more features for the number N of consecutive points in time. If radiological images are available that represent the examination area (at least partially) at other points in time, time series that show values of the one or more features for the number N of consecutive points in time can be generated by interpolation or extrapolation.
  • the time series are assigned to the M groups present in a classification process.
  • the minimum distance of a time series to a group center is greater than a predefined threshold, this is an indication that the corresponding sub-area shows a different dynamic behavior. It is an indication that the time series does not fit into the group. Since the time series has an even greater distance to the other groups, this is an indication that the time series does not fit into any group.
  • Time series that cannot be assigned to a group e.g. because the minimum distance to a group center is greater than a predefined threshold and/or because the distances to all group centers are greater than a predefined threshold, can be provided with a label indicating that the time series cannot be assigned.
  • the threshold can be set, for example, by a radiologist.
  • the threshold is set automatically by the computer system of the present disclosure. is set. It is conceivable, for example, that the threshold value is set based on statistical calculations. It is conceivable, for example, that for a group from whose group center a time series has a minimum distance, a standard deviation of the distances of all time series from the group center is calculated and the threshold value is a multiple (e.g. double or triple or another multiple) of the standard deviation.
  • outliers i.e. image elements that have an intensity value that differs significantly from the intensity values of neighboring image elements. Such outliers can, for example, be the result of disturbances in the generation of the radiological images.
  • time series are only marked as unassignable if there is a minimum number of neighboring image elements that cannot be assigned. This minimum number can be, for example, 10 or 20 or 100 or more or less. Individual outliers can, for example, be assigned to the group to which the majority of the time series from neighboring sub-areas are assigned. If time series are assigned to the M groups, the image elements of the time series can also be assigned to the M groups and marked with one of the M identifiers that represent the groups. In the case of time series that are marked as unassignable, the image elements of the time series can also be marked as unassignable.
  • the radiological images comprising the identifiers can be output and/or stored and/or transmitted to a separate computer system. If the radiological images comprising the identifiers are output (e.g. displayed on a screen or output on a printer), the image elements marked as unassignable can be displayed with a separate color tone, preferably with a color tone that is easily recognizable to humans in the context of the other colors of the radiological images. A user (e.g. radiologist) can then immediately see which image elements could not be assigned and which sub-areas therefore show a different dynamic behavior during the contrast-enhanced radiological examination.
  • Fig. 2 shows an example and schematic of the signal curve of various abnormalities in radiological images.
  • Fig. 1 shows the signal curve A in the hepatic artery from Fig. 1 as a reference.
  • the T1w signal intensities are plotted as intensity values on the ordinate.
  • the times are plotted on the abscissa.
  • the signal curve marked with (a) shows the signal curve of a focal nodular hyperplasia (FNH).
  • the signal curve marked with (b) shows the signal curve of a hepatocellular adenoma.
  • the signal curve marked with (c) shows the signal curve of a liver hemangioma.
  • the signal curve marked with (d) shows the signal curve of a hepatocellular carcinoma or of hypervascular metastases.
  • the signal curve marked with (e) shows the signal curve of hypovascular metastases. It can be seen that each abnormality (a) to (e) has a characteristic signal curve.
  • the signal curves of the abnormalities also differ from the signal curves of liver arteries, liver veins and healthy liver cells.
  • radiological images are received from an examination object.
  • the radiological images represent an examination area of the examination object at a number N of consecutive points in time at least after an application of a contrast agent.
  • Each radiological image comprises a large number of image elements.
  • Each image element represents a sub-area of the examination area.
  • For each image element of at least some of the sub-areas, one or more features are extracted from the radiological images.
  • Time series are generated for at least some of the sub-areas of the examination area based on the radiological images. Each time series comprises values of the one or more features for the number N of consecutive points in time.
  • the examination subject is again subjected to a contrast-enhanced radiological examination.
  • Further radiological images of the examination area of the examination subject are generated.
  • the radiological images represent the examination area at the number N of consecutive points in time at least after the application of a contrast agent.
  • the contrast agent is preferably the same as in the previous examination.
  • the generated radiological images are received by the computer system of the present disclosure.
  • Each radiological image comprises a plurality of image elements. Each image element represents a sub-area of the examination area.
  • a time series is generated based on the radiological images.
  • Each time series contains values of one or more characteristics for the number N of consecutive points in time.
  • the time series are assigned to the M groups in a classification process. If the minimum distance of a time series to a group focus is greater than a predefined threshold value during the assignment, this is an indication that the corresponding sub-area shows a different dynamic behavior. It is an indication that the time series does not fit into the group. Since the time series is even further away from the other groups, this is an indication that the time series does not fit into any group. Time series that cannot be assigned to a group, e.g.
  • the threshold can be set, for example, by a radiologist. It is also conceivable that the threshold is set automatically by the computer system of the present disclosure. If an individual time series cannot be clearly assigned to a group, the time series may include one or more outliers. Therefore, in one embodiment, time series are only marked as not assignable if there is a minimum number of neighboring image elements that are not assignable. If time series are assigned to the M groups, the image elements of the time series can also be assigned to the M groups and marked with one of the M labels representing the groups.
  • the image elements of the time series can also be marked as not assignable.
  • the radiological images comprising the characteristics can be output and/or stored and/or transmitted to a separate computer system. If the radiological images comprising the characteristics are output (e.g. displayed on a screen or output on a printer), the image elements marked as unassignable can be displayed with a separate color tone, preferably with a color tone that is easily recognizable to humans in the context of the other colors of the radiological images. A user (e.g. radiologist) can then immediately recognize which image elements were unassignable and which sub-areas therefore show a different dynamic behavior in the new contrast-enhanced radiological examination than in the previous radiological examination.
  • the user can inspect the sub-areas and look at the dynamic behavior of the contrast agent in these sub-areas by comparing the radiological images that represent the examination area at successive points in time in order to draw conclusions about the causes of the changed behavior.
  • lesions in the liver can be identified.
  • malignant tumors can be distinguished from benign lesions in the liver.
  • radiological images of at least one examination object are received that show at least one abnormality.
  • Such an abnormality can be a sign of a disease, e.g. one or more lesions and/or one or more tumors.
  • Such an abnormality affects one or more sub-areas of the examination area.
  • Such an abnormality is characterized by the fact that the one or more sub-areas show a characteristic behavior after the application of a contrast agent.
  • time series of this one or more sub-areas differ from time series of other sub-areas of the examination area.
  • the radiological images represent the examination area at a number N of consecutive points in time at least after an application of a contrast agent.
  • Each radiological image comprises a plurality of image elements.
  • Each image element represents a sub-area of the examination area.
  • Time series are generated for at least some of the sub-areas of the examination area based on the radiological images.
  • Each time series includes values of one or more features for the number N of consecutive points in time.
  • the time series are grouped in a cluster analysis into a number M of groups, as described in this description.
  • the number of groups is chosen so that time series from sub-areas that have one or more abnormalities fall into one group.
  • M groups that can be used for classification.
  • radiological images are received from another examination object.
  • the radiological images represent the examination area of the other examination object at the number N of consecutive points in time at least after the application of a contrast agent.
  • Each radiological image includes a large number of image elements. Each image element represents a sub-area of the examination area of the other examination object.
  • each image element For each image element, one or more features are extracted from the radiological images. For each sub-area of the examination area, a time series is generated based on the radiological images. Each time series includes values of one or more features for the number N of consecutive points in time.
  • the time series are assigned to the M groups in a classification process. If, during this classification, one or more time series are assigned to the group that represent time series of sub-areas with one or more abnormalities, then such abnormalities are also present in the radiological images of the other object under examination. If the time series are assigned to the M groups, the image elements of the time series can also be assigned to the M groups and marked with one of the M characteristics that represent the groups.
  • the radiological images comprising the characteristics can be output and/or saved and/or transmitted to a separate computer system.
  • the image elements that fall into the group of abnormalities can be displayed with their own color tone, preferably with a color tone that is easily recognizable to humans in the context of the other colors of the radiological images.
  • a user e.g. radiologist
  • the abnormalities that are included in the grouping can be, for example, focal nodular hyperplasias, hepatocellular adenomas, liver hemangiomas, hepatocellular carcinomas, hypervascular metastases and/or hypovascular metastases, because, as Fig. 2 shows, these show a characteristic signal curve that can generate their own group when grouped.
  • Fig. 3 shows an example and schematically an embodiment of the computer-implemented method of the present disclosure in the form of a flow chart.
  • the method (100) comprises the following steps: (110) receiving radiological images, the radiological images representing an examination area of at least one examination object at a number N of consecutive points in time at least after an application of a contrast agent, each radiological image comprising a plurality of image elements, each image element representing a sub-area of the examination area, (120) for each image element: extracting one or more features from the radiological images, (130) generating a time series for each sub-area of the examination area, each time series of a sub-area comprising the one or more extracted features of the image elements representing the sub-area for the number N of consecutive points in time, (140) selecting at least some of the time series, (150) grouping the selected time series into a number M of groups, (160) assigning each non-selected time series to one of the M groups, (170) assigning the image elements of each time series to the group to
  • Fig. 4 shows by way of example and schematically the generation of time series and the grouping of time series.
  • a first step three radiological images are received, a first radiological image I 1 , a second radiological image I 2 and a third radiological image I 3 .
  • I 1 , I 2 and I 3 are two-dimensional raster graphics.
  • Each of the radiological images I1, I2 and I3 represents the examination area of an object under examination.
  • the radiological images I 1 , I 2 and I 3 represent the examination area of the object under examination at different times.
  • the first radiological image I 1 represents the investigation area of the object under investigation at a first time t 1 .
  • the second radiological image I 2 represents the investigation area of the object under investigation at a second time t 2
  • the third radiological image I3 represents the examination area of the object under examination at a third time t 3 .
  • Each of the radiological images I1, I2 and I3 comprises a large number of image elements.
  • each of the radiological images I 1 , I 2 and I 3 three image elements are highlighted and provided with reference symbols.
  • the first radiological image I1 comprises the three image elements IE 11 , IE 12 and IE 13 (and other image elements).
  • the second radiological image I 2 includes the three image elements IE 21 , IE 22 and IE 23 (and other image elements).
  • the third radiological image I 3 includes the three image elements IE 31 , IE 32 and IE 33 (and other image elements).
  • Each image element of each radiological image represents a sub-area of the examination area of the object under examination.
  • the image elements IE 11 , IE 21 and IE 31 correspond to each other, i.e. they represent the same part of the examination area of the object under investigation.
  • the image elements IE12, IE22 and IE32 also correspond to each other; they also represent the same part of the examination area of the object under investigation; however, this part of the area differs from the part of the image elements IE 11 , IE 21 and IE 31 represent.
  • the image elements IE also correspond 13 , IE 23 and IE 33 with each other; they also represent the same part of the examination area of the object under investigation; this part, however, differs from the part of the image elements IE 11 , IE 21 and IE 31 and from the part of the image elements IE12, IE22 and IE32 represent.
  • the image elements IE 11 , IE 21 and IE 31 represent the subarea T 1 .
  • the image elements IE 12 , IE 22 and IE 32 represent the subarea T 2 .
  • the image elements IE 13 , IE 23 and IE 33 represent the subarea T 3 .
  • the image element IE 11 represents the subarea T 1 at time t 1 .
  • the image element IE 21 represents the sub-area T1 at time t2.
  • the image element IE31 represents the sub-area T1 at time t 3 .
  • the image element IE 12 represents the subarea T 2 at time t 1 .
  • the image element IE 22 represents the subarea T 2 at time t 2 .
  • the image element IE 32 represents the subarea T 2 at time t 3 .
  • the image element IE 13 represents the subarea T 2 at time t 1 .
  • the image element IE 23 represents the subarea T 2 at time t 2 .
  • the image element IE33 represents the partial area T2 at time t3.
  • one or more features are extracted from the radiological images for each image element.
  • the extracted feature is a hatching with which the respective image element is represented.
  • the image element IE 11 is shown with a hatching, which in the example shown in Fig.4 is referred to as feature M2.
  • the image element IE 21 is shown with a hatching, which in the example shown in Fig.4 is referred to as feature M 3
  • the image element IE 31 is shown with a hatching, which in the example shown in Fig.4 is referred to as feature M 1
  • feature M 1 For the image element IE 31
  • the feature M 1 extracted from the radiological image I3.
  • the image element IE 12 is shown with a hatching, which in the example shown in Fig.4 is referred to as feature M 2
  • the image element IE 12 The feature M 2 from radiological image I 1 extracted.
  • the image element IE 22 is shown with a hatching, which in the example shown in Fig.4 is referred to as feature M3.
  • feature M3 For the image element IE22, the feature M3 from the radiological image I 2 extracted.
  • the image element IE 32 is shown with a hatching, which in the example shown in Fig.4 is referred to as feature M 1 For the image element IE 32 The feature M 1 from radiological image I 3 extracted.
  • the image element IE13 is shown with a hatching, which in the example shown in Fig.4 is referred to as feature M 1 For the image element IE 13 The feature M 1 from radiological image I 1 extracted.
  • the image element IE 23 is shown with a hatching, which in the example shown in Fig.4 is referred to as feature M 1 For the image element IE 23 The feature M 1 from radiological image I 2 extracted.
  • the image element IE33 is shown with a hatching, which in the example shown in Fig.4 is represented as feature M 1
  • the feature M 1 from radiological image I 3 extracted.
  • a time series is generated for each sub-area of the examination area. Each time series contains the extracted feature for the number of consecutive points in time. The time series are shown graphically in Fig.4.
  • the time series XT1 for the sub-area T1 contains the feature M2 of the image element IE11 at time t 1 , the feature M 3 of the image element IE 21 at time t 2 and the feature M 1 of the image element IE 31 at time t 3 .
  • the time series X T2 for sub-area T 2 includes the feature M 2 of the image element IE 12 at time t 1 , the feature M 3 of the image element IE 22 at time t 2 and the feature M 1 of the image element IE 32 at time t 3 .
  • the time series X T3 for sub-area T 3 includes the feature M 1 of the image element IE 13 at time t1, the feature M1 of the image element IE23 at time t2 and the feature M1 of the image element IE33 at time t 3 .
  • the time series for the sub-area T 1 and sub-area T 2 identical; they are assigned to the same group C 1
  • all image elements of all radiological images are assigned to the group to which the time series of the sub-area that the image elements represent is assigned. This is not shown in Fig.4.
  • the image elements IE 11 , IE 21 and IE 31 represent the subarea T 1 .
  • the time series X T1 of sub-area T 1 was assigned to Group C 1 assigned; this also includes the image elements IE 11 , IE 21 and IE 31 Group C 1
  • the image elements IE 12 , IE 22 and IE 32 represent the subarea T 2 .
  • the time series X T2 of the sub-area T2 was assigned to group C1; this also includes the image elements IE12, IE22 and IE 32 Group C 1
  • the image elements IE 13 , IE 23 and IE 33 represent the subarea T 3 .
  • the time series X T3 of sub-area T 3 was assigned to Group C 2 assigned; this also includes the image elements IE 13 , IE 23 and IE 33 Group C 2 assigned.
  • each image element is assigned a label, with each label representing the group to which the respective image element was assigned.
  • image elements that were assigned to the same group receive the same label; image elements that were assigned to different groups usually (but not necessarily) receive different labels.
  • the image elements IE 11 , IE 21 , IE 31 , IE 12 , IE 22 and IE 32 receive the same identifier because they have been assigned to the same group C1.
  • the sub-areas T represented by the image elements IE11, IE21, IE31, IE12, IE22 and IE32 1 and T 2 show a comparable (in the case of the example shown in Fig.4 identical) dynamic (temporal) behavior.
  • the image elements IE 13 , IE 23 and IE 33 also receive the same identifier because they were assigned to the same group C2.
  • the sub-area T3 represented by the image elements IE13, IE23 and IE33 shows a different dynamic (temporal) behavior than the sub-areas T 1 and T 2 .
  • the sub-area T 3 can be shown differently in a radiological image than the subareas T 1 and T 2 .
  • the radiological images can be output including the characteristics (e.g. displayed on a monitor or printed out with a printer), stored on a data storage device and/or transmitted to a separate computer system (e.g. via a network).
  • all image elements to which the same characteristic has been assigned can be displayed in the same way (e.g. in the same color and/or brightness) on a monitor and/or printed out on a printer so that a radiologist can recognize which sub-areas show comparable (e.g. identical) dynamic behavior and which sub-areas differ in their dynamic behavior.
  • Further embodiments of the present disclosure are: 1.
  • a computer-implemented method for segmenting a plurality of radiological images comprising: • receiving the plurality of radiological images, the radiological images representing an examination area of an examination subject at a number N of consecutive points in time at least after an application of a contrast agent, each radiological image comprising a plurality of image elements, each image element representing a partial area of the examination area, • for each image element: o for each point in time of the N consecutive points in time: extracting one or more features from the radiological images, o generating a time series based on the one or more extracted features, • assigning each time series to one of M groups, the assigning comprising: o selecting at least a portion of the time series, o grouping the selected time series into a number of M groups, o assigning each non-selected time series to one of the M groups, • assigning each image element to that group of the M groups to which the time series of the image element is assigned, • assigning a label of M identifiers for each image element, each identifier of
  • a portion of the radiological images represents the examination area of the examination object before application of the contrast agent and a portion of the radiological images represents the examination area of the examination object after application of the contrast agent.
  • the examination object is a mammal, preferably a human
  • the examination area is a part of the examination object, preferably the liver, the brain, the heart, the kidney, the lung, the stomach, the intestine or a part of the organs mentioned or several organs or another part of the body of the examination object.
  • the examination object is a human and the examination area is or includes the liver of the human. 5.
  • the radiological images are MRI images.
  • the contrast agent is a hepatobiliary contrast agent.
  • the number N of consecutive points in time is in the range from 5 to 20.
  • the one or more features is a signal intensity, the signal intensity preferably being represented by a shade of gray or color.
  • step of grouping the selected time series into a number M of groups comprises: (1) randomly forming a number M of group centers, (2) assigning each time series to the group center to which it has a minimum distance, (3) recalculating the group center for each group, (4) repeating steps (2) and (3) as often as the assignment of the time series to the groups changes.
  • step of assigning each non-selected time series to one of the M groups comprises: - calculating a distance of the time series to the centers of the groups, - assigning the time series to the group to whose center it has the minimum distance. 11.
  • a computer-implemented method for identifying abnormalities in radiological images comprising: • receiving and/or generating radiological images, the radiological images representing an examination area of at least one healthy examination subject at a number N of consecutive points in time at least after an application of the contrast agent, each radiological image comprising a plurality of image elements, each image element representing a partial area of the examination area, • for each image element: o for each point in time of the N consecutive points in time: extracting one or more features from the radiological images, o generating a time series based on the one or more extracted features, • Assigning each time series to one of M groups, the assignment comprising: o selecting at least a portion of the time series, o grouping the selected time series into a number of M groups, • receiving and/or generating further radiological images, the further radiological images representing the examination area of a further examination object at the number N of consecutive points in
  • the radiological images and the further radiological images represent the examination area before and after the application of the contrast agent.
  • the examination object is a mammal, preferably a human
  • the examination area is a part of the examination object, preferably the liver, the brain, the heart, the kidney, the lung, the stomach, the intestine or a part of the organs mentioned or several organs or another part of the body of the examination object.
  • the examination object is a human and the examination area is or comprises the human's liver.
  • the radiological images and the further radiological images are MRI images. 17.
  • the contrast agent is a hepatobiliary contrast agent. 18.
  • step of grouping the selected time series into a number M of groups comprises: (1) randomly forming a number M of group centers, (2) assigning each time series to the group center to which it has a minimum distance, (3) Recalculate the group center for each group, (4) Repeat steps (2) and (3) as often as the assignment of the time series to the groups changes. 21.
  • a computer-implemented method for identifying abnormalities in radiological images comprising: • receiving and/or generating radiological images, the radiological images representing an examination area of a healthy examination subject at a number N of consecutive points in time at least after an application of the contrast agent, each radiological image comprising a plurality of image elements, each image element representing a partial area of the examination area, • for each image element: o for each point in time of the N consecutive points in time: extracting one or more features from the radiological images, o generating a time series based on the one or more extracted features, • assigning each time series to one of M groups, the assigning comprising: o selecting at least a portion of the time series, o grouping the selected time series into a number of M groups, • receiving and/or generating further radiological images, the further radiological images representing the examination area of the examination subject at a number N of later consecutive points in time at least after the application of the contrast agent, each further radiological image comprises a plurality of image elements, wherein each image element
  • the radiological images and the further radiological images represent the examination region before and after the application of the contrast agent.
  • the examination object is a mammal, preferably a human
  • the examination area is a part of the examination object, preferably the liver, the brain, the heart, the kidney, the lung, the stomach, the intestine or a part of the organs mentioned or several organs or another part of the body of the examination object.
  • 24. The method according to one of embodiments 21 to 23, wherein the examination object is a human and the examination area is or includes the liver of the human.
  • the radiological images and the further radiological images are MRI images. 26.
  • the contrast agent is a hepatobiliary contrast agent.
  • the number N of consecutive points in time is in the range from 5 to 20.
  • the one or more features is a signal intensity, wherein the signal intensity is preferably represented by a shade of gray or color. 29.
  • step of grouping the selected time series into a number M of groups comprises: (1) randomly forming a number M of group centers, (2) assigning each time series to the group center to which it has a minimum distance, (3) recalculating the group center for each group, (4) repeating steps (2) and (3) as often as the assignment of the time series to the groups changes.
  • a computer-implemented method for identifying abnormalities in radiological images comprising: • receiving and/or generating radiological images, the radiological images representing an examination area of at least one examination object at a number N of consecutive points in time at least after an application of the contrast agent, each radiological image comprising a plurality of image elements, each image element representing a partial area of the examination area, the examination area of the at least one examination object having an abnormality that can be attributed to a disease of the at least one examination object, • for each image element: o for each point in time of the N consecutive points in time: extracting one or more features from the radiological images, o generating a time series based on the one or more extracted features, • assigning each time series to one of M groups, the assigning comprising: o selecting at least a portion of the time series, o grouping the selected time series into a number of M groups, at least one group having time series of such image elements that cover at least a partial area of the examination area with the Represent abnormality, • Receiving and
  • the radiological images and the further radiological images represent the examination area before and after the application of the contrast agent.
  • the examination object is a mammal, preferably a human
  • the examination area is a part of the examination object, preferably the liver, the brain, the heart, the kidney, the lung, the stomach, the intestine or a part of the organs mentioned or several organs or another part of the body of the examination object.
  • the examination subject is a human and the examination region is or comprises the liver of the human.
  • the radiological images and the further radiological images are MRI images. 35.
  • the contrast agent is a hepatobiliary contrast agent.
  • the number N of consecutive points in time is in the range from 5 to 20.
  • the one or more features is a signal intensity, wherein the signal intensity is preferably represented by a shade of gray or color. 38.
  • step of grouping the selected time series into a number M of groups comprises: (1) randomly forming a number M of group centers, (2) assigning each time series to the group center to which it has a minimum distance, (3) recalculating the group center for each group, (4) repeating steps (2) and (3) as often as the assignment of the time series to the groups changes.
  • Computer system comprising means for carrying out a method according to any one of embodiments 1 to 38.
  • Computer program product comprising a data store in which a computer program is stored that can be loaded into a working memory of a computer system and causes the computer system to carry out a method according to any one of embodiments 1 to 38.
  • a "computer system” is a system for electronic data processing that processes data using programmable calculation rules. Such a system usually comprises a “computer”, the unit that includes a processor for carrying out logical operations, and a peripheral.
  • "peripherals” refer to all devices that are connected to the computer and are used to control the computer and/or as input and output devices. Examples of these are monitors (screens), printers, scanners, mice, keyboards, drives, cameras, microphones, loudspeakers, etc. Internal connections and expansion cards are also considered peripherals in computer technology.
  • Fig. 5 shows an example and schematically a computer system according to the present disclosure. The computer system (1) shown in Fig.
  • the control and computing unit (20) is used to control the computer system (1), coordinate the data flows between the units of the computer system (1) and carry out calculations.
  • the control and computing unit (20) is configured to • cause the input unit (10) to receive radiological images, the radiological images representing an examination area of at least one examination object at a number N of consecutive points in time at least after an application of a contrast agent, each radiological image comprising a plurality of image elements, each image element representing a sub-area of the examination area, • generate a time series for each sub-area of the examination area based on the radiological images, each time series comprising intensity values for the number N of consecutive points in time, • select at least some of the time series, • group the selected time series into a number M of groups, • assign each non-selected time series to one of the M groups, • assign the image elements of each time series to the group to which the time series is assigned, • assign one of M identifiers to each image element, each
  • Fig. 6 shows, by way of example and schematically, another embodiment of the computer system according to the invention.
  • the computer system (1) comprises a processing unit (21) which is connected to a memory (22).
  • the processing unit (21) and the memory (22) form a control and calculation unit as shown in Fig. 5.
  • the processing unit (21) can comprise one or more processors alone or in combination with one or more memories.
  • the processing unit (21) can be conventional computer hardware which is capable of processing information such as digital images, computer programs and/or other digital information.
  • the processing unit (21) usually consists of an arrangement of electronic circuits, some of which are implemented as an integrated circuit or as several interconnected integrated circuits. circuits (an integrated circuit is sometimes also referred to as a "chip").
  • the processing unit (21) may be configured to execute computer programs that may be stored in a working memory of the processing unit (21) or in the memory (22) of the same or another computer system.
  • the memory (22) may be ordinary computer hardware capable of storing information such as digital images (e.g. representations of the examination area), data, computer programs and/or other digital information either temporarily and/or permanently.
  • the memory (22) may comprise volatile and/or non-volatile memory and may be permanently installed or removable. Examples of suitable memories are RAM (Random Access Memory), ROM (Read-Only Memory), a hard disk, flash memory, a removable computer diskette, an optical disc, a magnetic tape or a combination of the above.
  • the optical discs may include read-only compact discs (CD-ROM), read/write compact discs (CD-R/W), DVDs, Blu-ray discs, and the like.
  • the processing unit (21) may also be connected to one or more interfaces (11, 12, 31, 32, 33) to display, transmit, and/or receive information.
  • the interfaces may include one or more communication interfaces (32, 33) and/or one or more user interfaces (11, 12, 31).
  • the one or more communication interfaces may be configured to send and/or receive information, e.g., to and/or from an MRI scanner, a CT scanner, an ultrasound camera, other computer systems, networks, data storage, or the like.
  • the one or more communication interfaces may be configured to transmit and/or receive information via physical (wired) and/or wireless communication links.
  • the one or more communication interfaces may include one or more interfaces for connecting to a network, e.g., using technologies such as cellular, Wi-Fi, satellite, cable, DSL, fiber optic, and/or the like.
  • the one or more communication interfaces may include one or more short-range communication interfaces configured to connect devices using short-range communication technologies such as NFC, RFID, Bluetooth, Bluetooth LE, ZigBee, infrared (e.g., IrDA), or the like.
  • the user interfaces may include a display (31).
  • a display (31) may be configured to display information to a user.
  • Suitable examples include a liquid crystal display (LCD), a light-emitting diode display (LED), a plasma display panel (PDP), or the like.
  • the user input interface(s) (11, 12) may be wired or wireless and may be configured to receive information from a user into the computer system (1), e.g. for processing, storage and/or display.
  • Suitable examples of user input interfaces are a microphone, an image or video capture device (e.g. a camera), a keyboard or keypad, a joystick, a touch-sensitive surface (separate from or integrated with a touchscreen), or the like.
  • the user interfaces may include automatic identification and data capture (AIDC) technology for machine-readable information.
  • AIDC automatic identification and data capture
  • the user interfaces may further include one or more interfaces for communicating with peripheral devices such as printers and the like.
  • One or more computer programs (40) can be stored in the memory (22) and executed by the processing unit (21), which is thereby programmed to perform the functions described in this description.
  • the retrieval, loading and execution of instructions of the computer program (40) can be carried out sequentially, so that one command is retrieved, loaded and executed at a time. However, the retrieval, loading and/or execution can also be carried out in parallel.
  • the system according to the invention can be designed as a laptop, notebook, netbook and/or tablet PC, it can also be a component of an MRI scanner, a CT scanner or an ultrasound diagnostic device.
  • the present invention also relates to a computer program product.
  • a computer program product comprises a non-volatile data carrier such as a CD, a DVD, a USB stick or another medium for storing data.
  • a computer program is stored on the data carrier.
  • the computer program can be loaded into a working memory of a computer system (in particular into a working memory of a computer system of the present disclosure) and cause the computer system to carry out the following steps: • receiving radiological images, the radiological images representing an examination area of at least one examination object at a number N of consecutive points in time at least after an application of a contrast agent, each radiological image comprising a plurality of image elements, each image element representing a sub-area of the examination area, • for each image element: extracting one or more features from the radiological images, • generating a time series for each sub-area of the examination area, each time series of a sub-area comprising the one or more extracted features of the image elements representing the sub-area for the number N of consecutive points in time, • selecting at least some of the time series, • grouping the selected time series into a number M of groups, • assigning each non-selected time series to one of the M groups, • assigning the image elements of each time series to the group to which the time series is assigned, • assigning a
  • the computer program product can also be marketed in combination (in a compilation) with the contrast agent.
  • a compilation is also referred to as a kit.
  • a kit comprises the contrast agent and the computer program product.
  • These means can comprise a link, i.e. an address of the internet page from which the computer program can be obtained, e.g. from which the computer program can be downloaded to a computer system connected to the internet.
  • These means can comprise a code (e.g.
  • kits are therefore a combination product comprising a contrast agent and a computer program (e.g. in the form of access to the computer program or in the form of executable program code on a data carrier) that is offered for sale together.

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Abstract

La présente invention concerne l'analyse d'images radiologiques. L'invention concerne entre autres un procédé mis en oeuvre par ordinateur, un système informatique et un programme informatique pour segmenter des images radiologiques et/ou pour identifier des anomalies dans des images radiologiques.
PCT/EP2023/076726 2022-10-17 2023-09-27 Analyse automatisée d'images radiologiques WO2024083466A1 (fr)

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