MRI segmentation
64 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in MRI segmentation
SKM-TEA
ABIDE
Most implemented papers
A Learning Strategy for Contrast-agnostic MRI Segmentation
BBillot/SynthSeg
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MIDL 2019
These samples are produced using the generative model of the classical Bayesian segmentation framework, with randomly sampled parameters for appearance, deformation, noise, and bias field.
Acute and sub-acute stroke lesion segmentation from multimodal MRI
NIC-VICOROB/SUNet-architecture
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Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed treatment decisions.
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
ad12/DOSMA
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Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
LucasFidon/label-set-loss-functions
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Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images.
Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI Datasets
linxuanhan/e-mim
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Overall, we construct the enhanced MIM (E-MIM) with HMP and PBT module to avoid model collapse multi-modal MRI.
3D Densely Convolutional Networks for Volumetric Segmentation
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
3D Densely Convolutional Networks for VolumetricSegmentation
black0017/MedicalZooPytorch
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arXiv preprint 2017
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network
Neonatal brain segmentation in magnetic resonance (MR) is a challenging problem due to poor image quality and low contrast between white and gray matter regions.
Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation
We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.
