MRI segmentation
44 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in MRI segmentation
SKM-TEA
ABIDE
Most implemented papers
Cardiac MRI Segmentation with Strong Anatomical Guarantees
vitalab/castor
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In this paper, we propose a cardiac MRI segmentation method which always produces anatomically plausible results.
Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation
MIPT-Oulu/RobustCartilageSegmentation
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Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA) research via magnetic resonance imaging (MRI).
Distributionally Robust Deep Learning using Hardness Weighted Sampling
LucasFidon/HardnessWeightedSampler
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In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM).
Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
liuquande/SAML
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We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation.
PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation
harveerar/PSIGAN
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Our method achieved an overall average DSC of 0. 87 on T1w and 0. 90 on T2w for the abdominal organs, 0. 82 on T2wFS for the parotid glands, and 0. 77 on T2w MRI for lung tumors.
Learning Directional Feature Maps for Cardiac MRI Segmentation
Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters.
Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation
neuro-ml/domain_shift_anatomy
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Domain Adaptation (DA) methods are widely used in medical image segmentation tasks to tackle the problem of differently distributed train (source) and test (target) data.
Hierarchical 3D Feature Learning for Pancreas Segmentation
We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans.
HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation
Particularly, for the Extrinsic Consistency, we leverage the knowledge across multiple source domains to enforce data-level consistency.
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
Motivated by atlas-based segmentation, we propose a novel volumetric self-supervised learning for data augmentation capable of synthesizing volumetric image-segmentation pairs via learning transformations from a single labeled atlas to the unlabeled data.
