Zhu et al., 2022 - Google Patents
OASIS: One-pass aligned atlas set for medical image segmentationZhu et al., 2022
- Document ID
- 17517914835148569194
- Author
- Zhu Q
- Wang Y
- Du B
- Yan P
- Publication year
- Publication venue
- Neurocomputing
External Links
Snippet
Medical image segmentation is a fundamental task in medical image analysis. Despite that deep convolutional neural networks have gained stellar performance in this challenging task, they typically rely on large labeled datasets, which have limited their extension to …
- 238000003709 image segmentation 0 title abstract description 43
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
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- 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
- G06T2207/30048—Heart; Cardiac
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- 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
- G06T2207/10024—Color image
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/0068—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping
- G06T3/0081—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping by elastic snapping
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Fan et al. | Adversarial learning for mono-or multi-modal registration | |
Jiao et al. | Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation | |
Crimi et al. | Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I | |
Yang et al. | MRI cross-modality image-to-image translation | |
Elazab et al. | GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images | |
Tang et al. | DA-DSUnet: dual attention-based dense SU-net for automatic head-and-neck tumor segmentation in MRI images | |
Jue et al. | Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation | |
Cheng et al. | Cortical surface registration using unsupervised learning | |
Andrade et al. | A practical review on medical image registration: From rigid to deep learning based approaches | |
Zhu et al. | OASIS: One-pass aligned atlas set for medical image segmentation | |
Zhu et al. | Multi-modal AD classification via self-paced latent correlation analysis | |
Dorent et al. | Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets | |
Ho et al. | Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images | |
Ni et al. | Segmentation of ultrasound image sequences by combing a novel deep siamese network with a deformable contour model | |
Esmaeili et al. | Generative adversarial networks for anomaly detection in biomedical imaging: A study on seven medical image datasets | |
Raviv et al. | Multi-modal brain tumor segmentation via latent atlases | |
Zhang et al. | Learning correspondences of cardiac motion from images using biomechanics-informed modeling | |
Lv et al. | A hybrid hemodynamic knowledge-powered and feature reconstruction-guided scheme for breast cancer segmentation based on DCE-MRI | |
Yousefirizi et al. | GAN-based bi-modal segmentation using mumford-shah loss: Application to head and neck tumors in PET-CT images | |
Kuang et al. | MSCDA: Multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets | |
Yao et al. | Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network | |
Liu et al. | LLRHNet: multiple lesions segmentation using local-long range features | |
Roth et al. | Cardiac segmentation of LGE MRI with noisy labels | |
Liu et al. | AHU-MultiNet: Adaptive loss balancing based on homoscedastic uncertainty in multi-task medical image segmentation network | |
Xu et al. | Bi-MGAN: bidirectional T1-to-T2 MRI images prediction using multi-generative multi-adversarial nets |