Lee et al., 2023 - Google Patents
Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapyLee et al., 2023
View HTML- Document ID
- 16409626498134524415
- Author
- Lee D
- Alam S
- Jiang J
- Cervino L
- Hu Y
- Zhang P
- Publication year
- Publication venue
- Medical physics
External Links
Snippet
Purpose To simultaneously register all the longitudinal images acquired in a radiotherapy course for analyzing patients' anatomy changes for adaptive radiotherapy (ART). Methods To address the unique needs of ART, we designed Seq2Morph, a novel deep learning …
Classifications
-
- 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
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
-
- 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
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Feng et al. | Deep convolutional neural network for segmentation of thoracic organs‐at‐risk using cropped 3D images | |
Fu et al. | LungRegNet: an unsupervised deformable image registration method for 4D‐CT lung | |
Andrearczyk et al. | Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images | |
Dong et al. | Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN | |
Zhu et al. | AnatomyNet: deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy | |
Zhong et al. | Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks | |
Liu et al. | CT‐based multi‐organ segmentation using a 3D self‐attention U‐net network for pancreatic radiotherapy | |
Zhong et al. | Boosting‐based cascaded convolutional neural networks for the segmentation of CT organs‐at‐risk in nasopharyngeal carcinoma | |
Zaidi et al. | Quantitative molecular positron emission tomography imaging using advanced deep learning techniques | |
Fu et al. | Deformable MR‐CBCT prostate registration using biomechanically constrained deep learning networks | |
Lin et al. | Deep learning for automatic target volume segmentation in radiation therapy: a review | |
Lee et al. | Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy | |
Lesjak et al. | Validation of white-matter lesion change detection methods on a novel publicly available MRI image database | |
Acosta et al. | Multi-atlas-based segmentation of pelvic structures from CT scans for planning in prostate cancer radiotherapy | |
Lei et al. | Male pelvic multi‐organ segmentation on transrectal ultrasound using anchor‐free mask CNN | |
Galib et al. | A fast and scalable method for quality assurance of deformable image registration on lung CT scans using convolutional neural networks | |
US11776128B2 (en) | Automatic detection of lesions in medical images using 2D and 3D deep learning networks | |
Bijar et al. | Atlas-based automatic generation of subject-specific finite element tongue meshes | |
EP3896649A1 (en) | Medical image synthesis of abnormality patterns associated with covid-19 | |
Peng et al. | H-SegMed: a hybrid method for prostate segmentation in TRUS images via improved closed principal curve and improved enhanced machine learning | |
US11717233B2 (en) | Assessment of abnormality patterns associated with COVID-19 from x-ray images | |
Xiao et al. | A review on 3D deformable image registration and its application in dose warping | |
Xie et al. | Low‐complexity atlas‐based prostate segmentation by combining global, regional, and local metrics | |
Sharif et al. | Artificial neural network‐based system for PET volume segmentation | |
CN108305279A (en) | A kind of brain magnetic resonance image super voxel generation method of iteration space fuzzy clustering |