[go: up one dir, main page]

Huff et al., 2023 - Google Patents

Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability

Huff et al., 2023

View PDF
Document ID
8222168603748715723
Author
Huff D
Santoro-Fernandes V
Chen S
Chen M
Kashuk C
Weisman A
Jeraj R
Perk T
Publication year
Publication venue
Physics in Medicine & Biology

External Links

Snippet

Objective. Patients with metastatic disease are followed throughout treatment with medical imaging, and accurately assessing changes of individual lesions is critical to properly inform clinical decisions. The goal of this work was to assess the performance of an automated …
Continue reading at iopscience.iop.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Health care, e.g. hospitals; Social work
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/103—Treatment planning systems
    • A61N5/1031—Treatment planning systems using a specific method of dose optimization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves

Similar Documents

Publication Publication Date Title
Polan et al. Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study
Cardenas et al. Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks
Ohri et al. Pretreatment 18F-FDG PET textural features in locally advanced non–small cell lung cancer: secondary analysis of ACRIN 6668/RTOG 0235
Ringl et al. The ribs unfolded-a CT visualization algorithm for fast detection of rib fractures: effect on sensitivity and specificity in trauma patients
Mattonen et al. Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer
Nguyen et al. Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography
Marcatili et al. Development and validation of RAYDOSE: a Geant4-based application for molecular radiotherapy
Shiri et al. Decentralized distributed multi-institutional PET image segmentation using a federated deep learning framework
Huff et al. Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability
WO2020219620A1 (en) Systems and methods for automated and interactive analysis of bone scan images for detection of metastases
US20100142774A1 (en) method, a system, and an apparatus for using and processing multidimensional data
Ebert et al. Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations
Mu et al. 18F-FDG PET/CT habitat radiomics predicts outcome of patients with cervical cancer treated with chemoradiotherapy
Helmbrecht et al. On the feasibility of automatic detection of range deviations from in-beam PET data
Besson et al. Rapid contour-based segmentation for 18F-FDG PET imaging of lung tumors by using ITK-SNAP: comparison to expert-based segmentation
Rahmim et al. A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction
Grimes et al. JADA: a graphical user interface for comprehensive internal dose assessment in nuclear medicine
Dewalle-Vignion et al. Is STAPLE algorithm confident to assess segmentation methods in PET imaging?
Balagurunathan et al. Semi‐automated pulmonary nodule interval segmentation using the NLST data
Gurney-Champion et al. A convolutional neural network for contouring metastatic lymph nodes on diffusion-weighted magnetic resonance images for assessment of radiotherapy response
Deeley et al. Segmentation editing improves efficiency while reducing inter-expert variation and maintaining accuracy for normal brain tissues in the presence of space-occupying lesions
Ren et al. Enhancing the reliability of deep learning-based head and neck tumour segmentation using uncertainty estimation with multi-modal images
Shao et al. Fast prediction of patient-specific organ doses in brain CT scans using support vector regression algorithm
Vazquez et al. A deep learning-based approach for statistical robustness evaluation in proton therapy treatment planning: a feasibility study
Finnegan et al. Localised delineation uncertainty for iterative atlas selection in automatic cardiac segmentation