CN111583189A - Brain nuclear mass positioning method and device, storage medium and computer equipment - Google Patents
Brain nuclear mass positioning method and device, storage medium and computer equipment Download PDFInfo
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Abstract
The application relates to a method, a device, a storage medium and a computer device for positioning a brain nucleus, which are different from a method needing to rely on doctor experience for positioning in the prior art, and the computer device is used for processing a brain image according to a template image containing a nucleus segmentation mask to determine the brain nucleus in the brain image, so that the positioning efficiency is higher, and the positioning efficiency of the brain nucleus can be improved. In addition, the nuclear group in the brain image can be accurately positioned by performing registration processing on the brain image and the template image and then determining the nuclear group in the brain image according to the nuclear group segmentation mask and the registration result, and compared with a manual positioning mode, the accuracy of the nuclear group positioning result can be effectively improved.
Description
Technical Field
The present application relates to the field of medical image technology, and in particular, to a method, an apparatus, a storage medium, and a computer device for brain nuclei localization.
Background
Parkinson's Disease (PD) is a common degenerative Disease of the nervous system, and the existing treatment regimen is usually treatment with Deep Brain Stimulation (DBS). Target nuclei commonly used for DBS treatment include the Subthalamic Nucleus (STN) and the Globus Pallidus Internas (GPI), among others.
In the DBS diagnosis process, the target nuclei need to be located, and the target nuclei of DBS are mostly distributed in the deep part of the brain. In the prior art, positioning is mostly carried out by depending on the experience of doctors, the positioning efficiency is low, and errors are easy to occur.
Disclosure of Invention
In view of the above, there is a need to provide a method, an apparatus, a storage medium, and a computer device for locating a brain nuclei, which are helpful to improve the locating efficiency and accuracy.
A method of brain nuclei localization comprising:
acquiring a first brain image and a template image containing a first nucleus segmentation mask;
determining a first positioning feature in the first brain image, and transforming the first brain image into a target coordinate space based on the first positioning feature to obtain a second brain image, wherein the target coordinate space is a coordinate space where the template image is located;
determining a second positioning feature in the second brain image, and registering the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain a transformation relation between the second brain image and the template image;
and determining the nuclei in the second brain image based on the transformation relation among the first nuclei segmentation mask, the second brain image and the template image.
A brain nuclei localization apparatus, comprising:
the image acquisition module is used for acquiring a first brain image and a template image containing a first nuclear group segmentation mask;
the first processing module is used for determining a first positioning feature in the first brain image, and transforming the first brain image into a target coordinate space based on the first positioning feature to obtain a second brain image, wherein the target coordinate space is a coordinate space where the template image is located;
the second processing module is used for determining a second positioning feature in the second brain image, and registering the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain a transformation relation between the second brain image and the template image;
and the nuclear group determining module is used for determining the nuclear group in the second brain image based on the transformation relation among the first nuclear group segmentation mask, the second brain image and the template image.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
The brain nuclear group positioning method, the device, the storage medium and the computer equipment are used for acquiring a first brain image and a template image containing a first nuclear group segmentation mask; determining a first positioning feature in the first brain image, and transforming the first brain image into a target coordinate space based on the first positioning feature to obtain a second brain image, wherein the target coordinate space is a coordinate space where the template image is located; determining a second positioning feature in the second brain image, and registering the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain a transformation relation between the second brain image and the template image; and determining the nuclei in the second brain image based on the transformation relation among the first nuclei segmentation mask, the second brain image and the template image.
The application provides a method for positioning a brain nucleus, which is different from a method needing to rely on doctor experience to position in the prior art, and a computer device is used for processing a brain image according to a template image containing a nucleus segmentation mask to determine the brain nucleus in the brain image, so that the positioning efficiency is high, and the method can improve the positioning efficiency of the brain nucleus. In addition, the method can accurately position the nuclear group in the brain image by performing registration processing on the brain image and the template image and then determining the nuclear group in the brain image according to the nuclear group segmentation mask and the registration result, and can effectively improve the accuracy of the nuclear group positioning result compared with a manual positioning mode.
Drawings
FIG. 1 is a schematic flow chart of a method for localizing brain nuclei in an embodiment;
FIG. 2 is a schematic representation of the Talairach coordinate system and a portion of a second locating feature in one embodiment;
FIG. 3 is a schematic diagram of the positional relationship of a second indexing feature to a first indexing feature in one embodiment;
FIG. 4 is a schematic diagram illustrating an embodiment of a process for transforming a first brain image into a target coordinate space to obtain a second brain image based on a first positioning feature;
fig. 5 is a schematic flow chart illustrating a transformation relationship between the second brain image and the template image obtained by registering the second brain image and the template image based on the first positioning feature and the second positioning feature according to an embodiment;
FIG. 6 is a diagram illustrating the sub-area division of the second brain image according to an embodiment;
fig. 7 is a schematic flow chart illustrating a transformation relationship between the second brain image and the template image obtained by registering the second brain image and the template image based on the first positioning feature and the second positioning feature according to another embodiment;
FIG. 8 is a flow diagram illustrating a process for determining nuclei in a second brain image according to one embodiment;
FIG. 9 is a schematic diagram of the structure of a device for localizing brain nuclei in an embodiment;
FIG. 10 is a diagram showing an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The technical scheme of the application can be applied to Deep Brain Stimulation (DBS), and the DBS is an effective means for treating Parkinson's Disease (PD), dystonia, depression, epilepsy and other neurological diseases. Long-term drug therapy can lead to patient resistance, and DBS is used instead of drug therapy. The success of the DBS procedure depends on the physician's ability to accurately locate the nucleus. The most commonly used target nuclei for DBS surgery in PD patients include the subthalamic nucleus (STN) and the Globus Pallidus Interna (GPI), both of which significantly improve the clinical symptoms in PD patients. Based on the problems of low efficiency and accuracy of the existing target nucleus positioning method, the application aims to improve the positioning efficiency and the accuracy of the positioning result of the target nucleus and further improve the success rate of the DBS operation.
It is understood that the technical solution of the present application is not limited to be applied to DBS, but may be applied to other technologies/methods that require brain cluster localization.
In one embodiment, as shown in fig. 1, a method for localizing a brain nucleus is provided, which is explained by taking an example of the method applied to a processor capable of localizing a brain nucleus, and the method mainly includes the following steps:
step S100, a first brain image and a template image including a first nuclei segmentation mask are obtained.
The first brain image acquired by the processor is a brain image of a subject needing a DBS operation, and the first brain image may specifically be an MRI (Magnetic Resonance Imaging) image. Specifically, the processor may obtain the first brain image by performing image reconstruction and correction on scan data acquired by the magnetic resonance scanning apparatus. Of course, the first brain image may be reconstructed and corrected in advance and stored in the memory of the computer device, and when it needs to be processed, the processor reads the first brain image directly from the memory of the computer device. Of course, the processor may also acquire the first brain image from an external device. For example, the first brain image is stored in the cloud, and when a processing operation is required, the processor acquires the first brain image from the cloud. In this embodiment, the acquisition manner of the processor for acquiring the first brain image is not limited.
In order to accurately locate the brain nuclei in the first brain image, the method uses an atlas registration mode to maximally register the first brain image and atlas data (namely a template image), and then a first nuclei segmentation Mask (Mask1) carried by the template image is spread onto the first brain image. The template image is an average atlas registered to the same space by a large amount of individual data so as to ensure the universality of the atlas. For example, the template image selected by the application is derived from 7.0T magnetic resonance scan data of a brain of 168 adults, and the 7.0T magnetic resonance scan data has a basal nucleus structure with clear boundaries, including a subthalamic nucleus, an internal globus pallidus nucleus and the like of target nuclei common to DBS surgery, so that the first nucleus segmentation mask in the template image can be accurately determined.
Step S200, determining a first positioning feature in the first brain image, and transforming the first brain image to a target coordinate space based on the first positioning feature to obtain a second brain image, wherein the target coordinate space is a coordinate space where the template image is located.
After the processor acquires the first brain image and the template image, the processor firstly performs space coordinate transformation of the images, thereby facilitating subsequent image registration. In this step, the processor performs coordinate space transformation on the first brain image based on the first positioning feature in the first brain image to obtain a second brain image consistent with the coordinate space of the template image. The target coordinate space may specifically be, for example, a Talairach coordinate space or the like.
Optionally, the first localization feature includes an Anterior union point (AC), a Posterior union Point (PC), and a mid-sagittal Plane (MSP). Wherein MSP represents the middle sagittal plane of the brain, which crosses the sulcus cerebri and bisects the brain into left and right hemispheres.
Specifically, the determination of the first positioning features AC, PC and MSP may be done by manual selection. The position of the MSP can be determined by first determining two anatomical landmark points, AC and PC, based on the first brain image, and then determining any point (IH) on the MSP that is not collinear with the AC-PC line. In addition, in order to realize the full automation of the whole process, the positioning of AC, PC and MSP can be automatically completed through an algorithm. Specifically, the MSP is used as a left-right bisection central axial plane of a cerebral hemisphere, and the MSP can be obtained by solving the symmetrical planes of the left and right hemispheres iteratively by a steepest descent method through a method for maximizing mutual information of the left and right hemispheres. The AC and PC are in the mid-sagittal plane and have distinct anatomical features, and template matching is performed in the mid-sagittal plane to determine the location of the AC and PC.
It can be understood that the determination of AC, PC and MSP can also be performed automatically by an algorithm, and then combined with manual confirmation and adjustment, thereby simplifying the processing flow and improving the efficiency while ensuring the accuracy.
Step S300, determining a second positioning feature in the second brain image, and registering the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain a transformation relation between the second brain image and the template image.
The processor transforms the first brain image to a target coordinate space based on the first positioning feature to obtain a second brain image, and then performs registration of the second brain image and the template image based on a second positioning feature of the second brain image and the first positioning feature.
Optionally, the second localization features include a front end Point (AP), a back end Point (PP), a left end Point (LP), a right end Point (Rightmost Point, RP), an upper end Point (Superior Point, SP), and a lower end Point (IP) of the brain. The second positioning features AP, PP, LP, RP, SP, IP represent points of the foremost, rearmost, leftmost, rightmost, uppermost, lowermost brain (without scalp and cerebrospinal fluid) in Talairach coordinate space, respectively.
Referring to fig. 2, the Talairach coordinate system uses AC as an origin, the direction from PC to AC is a Y-axis direction, the X-axis is perpendicular to MSP, and the Z-axis direction is determined according to the X-axis and the Y-axis.
As shown in fig. 3, which is a schematic diagram of the position relationship between the second positioning feature and the first positioning feature, referring to fig. 3, SP is directly above the PC, and IP is directly below the AC; LP and RP are on the left and right sides of the PC.
In particular, the determination of the second positioning features AP, PP, LP, RP, SP, IP may be done by manual selection. In addition, in order to realize the full automation of the whole process, the positioning of AP, PP, LP, RP, SP and IP can also be automatically completed through an algorithm. Specifically, the automatic positioning algorithm is implemented according to the following steps: (1) removing scalp and cerebrospinal fluid through a cortex segmentation algorithm to obtain a brain contour; (2) determining AP and PP respectively by the forward intersection point and the backward intersection point of the AC-PC connecting line and the brain outline; (3) determining SP through the intersection point of the upper line of the perpendicular line of the axial surface of the PC point and the outline of the brain; (4) determining an IP (Internet protocol) by a downward extension of a perpendicular line of an axial surface of the AC point; (5) the left and right intersection points of the perpendicular line through the mid-sagittal plane of the PC point with the brain contour determine LP and RP, respectively.
It can be understood that the determination of AP, PP, LP, RP, SP, and IP may also be performed automatically by an algorithm, and then combined with manual confirmation and adjustment, so that the accuracy can be ensured while simplifying the processing flow and improving the efficiency.
Step S400, determining the nuclear group in the second brain image based on the transformation relation among the first nuclear group segmentation mask, the second brain image and the template image.
The processor registers the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain a transformation relation between the second brain image and the template image, and then determines the nuclei in the second brain image based on the first nuclei segmentation mask and the transformation relation included in the template image. Specifically, the first nuclei segmentation mask included in the template image may be transformed based on the transformation relationship to obtain a transformed segmentation mask, and the transformed segmentation mask may represent the nuclei position in the second brain image, so that the nuclei in the second brain image may be determined based on the transformed segmentation mask.
The embodiment provides a method for locating a brain nucleus, which is different from a method needing to rely on doctor experience to locate in the prior art, but a computer device is used for processing a brain image according to a template image containing a nucleus segmentation mask to determine the brain nucleus in the brain image, and the locating efficiency is high, so that the method can improve the locating efficiency of the brain nucleus. In addition, the method can accurately position the nuclear group in the brain image by performing registration processing on the brain image and the template image and then determining the nuclear group in the brain image according to the nuclear group segmentation mask and the registration result, and can effectively improve the accuracy of the nuclear group positioning result compared with a manual positioning mode.
In one embodiment, as shown in fig. 4, in step S200, the first brain image is transformed into the target coordinate space based on the first positioning feature to obtain the second brain image, including steps S220 to S240.
Step S220, determining a transformation matrix of a current coordinate space and a target coordinate space according to the first positioning characteristics, wherein the current coordinate space is the coordinate space where the first brain image is located;
step S240, transforming the first brain image according to the transformation matrix to obtain a second brain image.
Specifically, based on the first positioning feature, which may be AC, PC, or MSP, a transformation matrix of a current coordinate space in which the first brain image is located and a target coordinate space in which the template image is located may be obtained, and then, the first brain image located in the current coordinate space is transformed based on the transformation matrix, so that a second brain image located in the target coordinate space may be obtained. Therefore, the transformation of the coordinate space is carried out, so that the subsequent registration processing of the image can be conveniently carried out, and the accuracy of the nuclear group positioning result is improved.
In one embodiment, as shown in fig. 5, in step S300, the second brain image and the template image are registered based on the first positioning feature and the second positioning feature to obtain a transformation relationship between the second brain image and the template image, which includes steps S320 to S340.
Step S320, dividing the second brain image into at least two sub-regions based on the first positioning feature and the second positioning feature;
step S340, carrying out scaling transformation on each corresponding area of each subregion in the template image respectively to obtain each transformed corresponding area with the same size as each subregion; the transformation relationship between the second brain image and the template image comprises the scaling of each corresponding region.
Fig. 6 is a schematic diagram illustrating the sub-area division of the second brain image. Specifically, based on the first positioning features AC, PC, MSP and the second positioning features AP, PP, LP, RP, SP, IP, the second brain image can be divided into 12 sub-regions, i.e. 6 regions for each of the left and right brains.
In order to achieve the maximum linear alignment of the second brain image and the template image, the present embodiment divides the template image and the second brain image into 12 sub-regions at the same time. And then obtaining the lengths of the AP-AC, AC-PC, PC-PP, SP-PC, IP-AC, RP-PC and LP-PC line segments in the template image and the second brain image respectively based on the second positioning features AP, PP, LP, RP, SP and IP in the template image and the second brain image. And finally, zooming each subregion in the template image according to the length of each line segment in the two images to ensure that the sizes of the corresponding subregions in the two images are the same, thereby realizing the piecewise linear alignment of the second brain image and the template image. The second brain image and the template image after the piecewise linear alignment are realized, and accurate registration can be obtained at each part of the brain, including a target nucleus concerned by DBS operation.
In one embodiment, as shown in fig. 7, the step S300 of registering the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain the transformation relationship between the second brain image and the template image further includes steps S360 to S380.
Step S360, respectively extracting a first target area in the sub-areas and a second target area in the transformed corresponding area, wherein the first target area and the second target area are image areas containing the same substrate nuclei;
s380, elastic registration based on gray information is carried out on the first target area and the second target area, and the registration deformation relation of the first target area and the second target area is determined; the transformation relationship between the second brain image and the template image further comprises a registration deformation relationship between the first target region and the second target region.
The alignment of the target nuclei positions on the second brain image and the template image can be realized through piecewise linear alignment, but the matching degree of the second brain image and the template image can be further improved through elastic registration because different individual nuclei still have certain difference in morphology. Since the DBS operation is only concerned with the basal nuclei, the elastic registration can be performed only in a local region including the basal nuclei, so that the amount of computation can be greatly reduced.
Specifically, for the second brain image and the scaled template image, the registration deformation relationship of the image regions containing the same basal ganglia in the two images can be calculated. The elastic registration based on the gray information may specifically be obtained by calculating mutual information of the two images as a similarity measurement index to optimize a registration deformation field, that is, obtaining a registration deformation relationship between the second brain image and the scaled template image. According to the method, on the basis of piecewise linear alignment, the accuracy of the registration result of the second brain image and the template image can be further improved by performing elastic registration of partial sub-regions.
In one embodiment, the step S400 of determining a nucleus in the second brain image based on the transformation relationship between the first nucleus segmentation mask, the second brain image and the template image specifically includes: and determining the nuclei in the second brain image based on the scaling of each corresponding region and the first nuclei segmentation mask, or based on the scaling of each corresponding region, the registration deformation relationship between the first target region and the second target region and the first nuclei segmentation mask.
Optionally, as shown in fig. 8, determining the nuclei in the second brain image based on the scaling of each corresponding region and the first nuclei segmentation mask, or based on the scaling of each corresponding region, the registration deformation relationship between the first target region and the second target region, and the first nuclei segmentation mask, includes steps S420 to S440.
Step S420, based on the scaling, or based on the scaling and the registration deformation relation, transforming the first kernel group segmentation mask to obtain a second kernel group segmentation mask;
step S440, the second brain image is segmented through the second nuclei segmentation mask to obtain nuclei in the second brain image.
Specifically, in the process of registering the second brain image and the template image, if only piecewise linear alignment is performed, the first nucleus segmentation mask is transformed based on the scaling to obtain a second nucleus segmentation mask; if the piecewise linear alignment and the elastic registration of partial sub-regions are carried out simultaneously, the first nucleus segmentation Mask is transformed based on the scaling and the registration deformation relation to obtain a second nucleus segmentation Mask (Mask2), so that the nucleus in the second brain image can be obtained based on the second nucleus segmentation Mask. Optionally, the opening operation smoothing boundary processing may be performed on the second kernel segmentation mask.
It should be understood that, under reasonable circumstances, although the steps in the flowcharts referred to in the foregoing embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in each flowchart may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 9, there is provided a brain nuclei localization apparatus, which mainly includes the following modules:
an image acquisition module 100, configured to acquire a first brain image and a template image including a first micelle segmentation mask;
the first processing module 200 is configured to determine a first positioning feature in the first brain image, transform the first brain image to a target coordinate space based on the first positioning feature, and obtain a second brain image, where the target coordinate space is a coordinate space where the template image is located;
the second processing module 300 is configured to determine a second positioning feature in the second brain image, and register the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain a transformation relationship between the second brain image and the template image;
the nuclei determining module 400 is configured to determine nuclei in the second brain image based on a transformation relationship between the first nuclei segmentation mask, the second brain image, and the template image.
The utility model provides a brain nuclear group positioner, the device is handled the brain image according to the template image that contains the nuclear group segmentation mask through computer equipment in order to confirm the nuclear group in the brain image, and its location efficiency is higher, and consequently, the device can improve the location efficiency of brain nuclear group. In addition, the device can accurately position the nuclear group in the brain image by performing registration processing on the brain image and the template image and then determining the nuclear group in the brain image according to the nuclear group segmentation mask and the registration result, and compared with a manual positioning mode, the device can effectively improve the accuracy of the nuclear group positioning result.
In one embodiment, the first processing module 200 is further configured to: determining a transformation matrix of a current coordinate space and a target coordinate space according to the first positioning characteristics, wherein the current coordinate space is a coordinate space where the first brain image is located; and transforming the first brain image according to the transformation matrix to obtain a second brain image.
In one embodiment, the second processing module 300 is further configured to: dividing the second brain image into at least two sub-regions based on the first positioning features and the second positioning features; respectively carrying out scaling transformation on each corresponding region of each subregion in the template image to obtain each transformed corresponding region with the same size as each subregion; the transformation relationship between the second brain image and the template image comprises the scaling of each corresponding region.
In one embodiment, the second processing module 300 is further configured to: respectively extracting a first target area in the sub-areas and a second target area in the corresponding area after transformation, wherein the first target area and the second target area are image areas containing the same substrate nuclei; elastic registration based on gray information is carried out on the first target area and the second target area, and the registration deformation relation of the first target area and the second target area is determined; the transformation relationship between the second brain image and the template image further comprises a registration deformation relationship between the first target region and the second target region.
In one embodiment, the clique determination module 400 is further operable to: and determining the nuclei in the second brain image based on the scaling of each corresponding region and the first nuclei segmentation mask, or based on the scaling of each corresponding region, the registration deformation relationship between the first target region and the second target region and the first nuclei segmentation mask.
In one embodiment, the clique determination module 400 is further operable to: transforming the first nuclear group segmentation mask based on the scaling or based on the scaling and the registration deformation relation to obtain a second nuclear group segmentation mask; and segmenting the second brain image through a second nuclear group segmentation mask to obtain a nuclear group in the second brain image.
For the specific definition of the brain nuclei localization apparatus, reference may be made to the above definition of the brain nuclei localization method, which is not described herein again. The modules in the brain nuclear localization device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring a first brain image and a template image containing a first nucleus segmentation mask; determining a first positioning feature in the first brain image, and transforming the first brain image into a target coordinate space based on the first positioning feature to obtain a second brain image, wherein the target coordinate space is a coordinate space where the template image is located; determining a second positioning feature in the second brain image, and registering the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain a transformation relation between the second brain image and the template image; and determining the nuclei in the second brain image based on the transformation relation among the first nuclei segmentation mask, the second brain image and the template image.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining a transformation matrix of a current coordinate space and a target coordinate space according to the first positioning characteristics, wherein the current coordinate space is a coordinate space where the first brain image is located; and transforming the first brain image according to the transformation matrix to obtain a second brain image.
In one embodiment, the processor, when executing the computer program, further performs the steps of: dividing the second brain image into at least two sub-regions based on the first positioning features and the second positioning features; respectively carrying out scaling transformation on each corresponding region of each subregion in the template image to obtain each transformed corresponding region with the same size as each subregion; the transformation relationship between the second brain image and the template image comprises the scaling of each corresponding region.
In one embodiment, the processor, when executing the computer program, further performs the steps of: respectively extracting a first target area in the sub-areas and a second target area in the corresponding area after transformation, wherein the first target area and the second target area are image areas containing the same substrate nuclei; elastic registration based on gray information is carried out on the first target area and the second target area, and the registration deformation relation of the first target area and the second target area is determined; the transformation relationship between the second brain image and the template image further comprises a registration deformation relationship between the first target region and the second target region.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and determining the nuclei in the second brain image based on the scaling of each corresponding region and the first nuclei segmentation mask, or based on the scaling of each corresponding region, the registration deformation relationship between the first target region and the second target region and the first nuclei segmentation mask.
In one embodiment, the processor, when executing the computer program, further performs the steps of: transforming the first nuclear group segmentation mask based on the scaling or based on the scaling and the registration deformation relation to obtain a second nuclear group segmentation mask; and segmenting the second brain image through a second nuclear group segmentation mask to obtain a nuclear group in the second brain image.
FIG. 10 is a diagram showing an internal structure of a computer device according to an embodiment. The computer device may specifically be a terminal (or server). As shown in fig. 10, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program which, when executed by the processor, causes the processor to implement the method for brain nuclei localization. The internal memory may also have stored therein a computer program that, when executed by the processor, causes the processor to perform a method of brain nuclei localization. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring a first brain image and a template image containing a first nucleus segmentation mask; determining a first positioning feature in the first brain image, and transforming the first brain image into a target coordinate space based on the first positioning feature to obtain a second brain image, wherein the target coordinate space is a coordinate space where the template image is located; determining a second positioning feature in the second brain image, and registering the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain a transformation relation between the second brain image and the template image; and determining the nuclei in the second brain image based on the transformation relation among the first nuclei segmentation mask, the second brain image and the template image.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a transformation matrix of a current coordinate space and a target coordinate space according to the first positioning characteristics, wherein the current coordinate space is a coordinate space where the first brain image is located; and transforming the first brain image according to the transformation matrix to obtain a second brain image.
In one embodiment, the computer program when executed by the processor further performs the steps of: dividing the second brain image into at least two sub-regions based on the first positioning features and the second positioning features; respectively carrying out scaling transformation on each corresponding region of each subregion in the template image to obtain each transformed corresponding region with the same size as each subregion; the transformation relationship between the second brain image and the template image comprises the scaling of each corresponding region.
In one embodiment, the computer program when executed by the processor further performs the steps of: respectively extracting a first target area in the sub-areas and a second target area in the corresponding area after transformation, wherein the first target area and the second target area are image areas containing the same substrate nuclei; elastic registration based on gray information is carried out on the first target area and the second target area, and the registration deformation relation of the first target area and the second target area is determined; the transformation relationship between the second brain image and the template image further comprises a registration deformation relationship between the first target region and the second target region.
In one embodiment, the computer program when executed by the processor further performs the steps of: and determining the nuclei in the second brain image based on the scaling of each corresponding region and the first nuclei segmentation mask, or based on the scaling of each corresponding region, the registration deformation relationship between the first target region and the second target region and the first nuclei segmentation mask.
In one embodiment, the computer program when executed by the processor further performs the steps of: transforming the first nuclear group segmentation mask based on the scaling or based on the scaling and the registration deformation relation to obtain a second nuclear group segmentation mask; and segmenting the second brain image through a second nuclear group segmentation mask to obtain a nuclear group in the second brain image.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, the computer program can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of localizing brain nuclei, comprising:
acquiring a first brain image and a template image containing a first nucleus segmentation mask;
determining a first positioning feature in the first brain image, and transforming the first brain image into a target coordinate space based on the first positioning feature to obtain a second brain image, wherein the target coordinate space is a coordinate space where the template image is located;
determining a second positioning feature in the second brain image, and registering the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain a transformation relation between the second brain image and the template image;
and determining the nuclei in the second brain image based on the transformation relation among the first nuclei segmentation mask, the second brain image and the template image.
2. The method of claim 1, wherein transforming the first brain image to a target coordinate space based on the first positioning feature, resulting in a second brain image, comprises:
determining a transformation matrix of a current coordinate space and the target coordinate space according to the first positioning characteristics, wherein the current coordinate space is a coordinate space where the first brain image is located;
and transforming the first brain image according to the transformation matrix to obtain the second brain image.
3. The method of claim 1, wherein registering the second brain image and the template image based on the first and second localization features, and obtaining a transformation relationship between the second brain image and the template image comprises:
dividing the second brain image into at least two sub-regions based on the first and second localization features;
respectively carrying out scaling transformation on each corresponding region of each sub-region in the template image to obtain each transformed corresponding region with the same size as each sub-region;
the transformation relation between the second brain image and the template image comprises the scaling of each corresponding region.
4. The method of claim 3, wherein registering the second brain image and the template image based on the first and second localization features further comprises:
respectively extracting a first target region in the sub-regions and a second target region in the transformed corresponding region, wherein the first target region and the second target region are image regions containing the same substrate nuclei;
elastic registration based on gray information is carried out on the first target area and the second target area, and the registration deformation relation of the first target area and the second target area is determined;
the transformation relationship between the second brain image and the template image further includes a registration deformation relationship between the first target region and the second target region.
5. The method of claim 4, wherein determining the nuclei in the second brain image based on the transformation relationship of the first nuclei segmentation mask, the second brain image and the template image comprises:
and determining the nuclei in the second brain image based on the scaling of each corresponding region and the first nuclei segmentation mask, or based on the scaling of each corresponding region, the registration deformation relationship between the first target region and the second target region, and the first nuclei segmentation mask.
6. The method of claim 5, wherein the determining the nuclei in the second brain image based on the scaling of each of the corresponding regions and the first nuclei segmentation mask or based on the scaling of each of the corresponding regions, the registered deformation relationship of the first target region and the second target region and the first nuclei segmentation mask comprises:
based on the scaling or the scaling and the registration deformation relation, transforming the first kernel group segmentation mask to obtain a second kernel group segmentation mask;
and segmenting the second brain image through the second nuclear group segmentation mask to obtain the nuclear group in the second brain image.
7. The method of any of claims 1-6, wherein the first positioning features include an anterior union point, a posterior union point, and a mid-sagittal plane;
the second localization features include a front endpoint, a back endpoint, a left endpoint, a right endpoint, an upper endpoint, and a lower endpoint of the brain.
8. A brain nuclei localization apparatus, comprising:
the image acquisition module is used for acquiring a first brain image and a template image containing a first nuclear group segmentation mask;
the first processing module is used for determining a first positioning feature in the first brain image, and transforming the first brain image into a target coordinate space based on the first positioning feature to obtain a second brain image, wherein the target coordinate space is a coordinate space where the template image is located;
the second processing module is used for determining a second positioning feature in the second brain image, and registering the second brain image and the template image based on the first positioning feature and the second positioning feature to obtain a transformation relation between the second brain image and the template image;
and the nuclear group determining module is used for determining the nuclear group in the second brain image based on the transformation relation among the first nuclear group segmentation mask, the second brain image and the template image.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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