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medical

MRI segmentation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in mri-segmentation

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Libraries

Use these libraries to find mri-segmentation models and implementations

Datasets

SKM-TEA

ABIDE

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Most implemented papers

Acute and sub-acute stroke lesion segmentation from multimodal MRI

Albert Clérigues, S. Valverde, J. Bernal, J. Freixenet, A. Oliver, X. Lladó•Tue Oct 30 2018

Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of class imbalance.

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Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

Adrian V. Dalca, M. Sabuncu, J. Guttag•Thu May 31 2018

A generative probabilistic model is introduced that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting to 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.

155 0

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Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

S. Ourselin, Tom Kamiel Magda Vercauteren, A. David, L. Fidon, M. Aertsen, D. Emam, N. Mufti, F. Guffens, T. Deprest, P. Demaerel, A. Melbourne, Jam Deprest•Wed Jul 07 2021

It is proved that there is one and only one method to convert a classical loss function for fully segmented images into a proper label-set loss function, and the leaf-Dice loss is defined, a label- set generalization of the Dice loss particularly suited for partial supervision with only missing labels.

29 0

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A Learning Strategy for Contrast-agnostic MRI Segmentation

Adrian V. Dalca, B. Fischl, J. E. Iglesias, Benjamin Billot, D. Greve, K. V. Leemput•Tue Mar 03 2020

A deep learning strategy that enables contrast-agnostic semantic segmentation of completely unpreprocessed brain MRI scans, without requiring additional training or fine-tuning for new modalities, and generalizes significantly better across datasets, compared to training using real images.

82 0

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The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

C. Igel, D. Torigian, Naji Khosravan, Arjun D Desai, G. Gold, B. Hargreaves, A. Chaudhari, M. Akçakaya, Mathias Perslev, Francesco Calivá, C. Iriondo, Aliasghar Mortazi, S. Jambawalikar, J. Ellerman, U. Bagci, R. Tibrewala, I. Flament, Matthew OBrien, S. Majumdar, A. Pai, E. Dam, S. Gaj, Mingrui Yang, Kunio Nakamura, Xiaojuan Li, C. Deniz, V. Juras, R. Regatte, V. Pedoia•Tue Apr 28 2020

Diverse networks learned to segment the knee similarly, where high segmentation accuracy did not correlate with cartilage thickness accuracy and voting ensembles did not exceed individual network performance.

70 0

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3D Densely Convolutional Networks for Volumetric Segmentation

Taesup Moon, T. Bui, Jitae Shin•Sun Sep 10 2017

A novel very deep network architecture based on a densely convolutional network for volumetric brain segmentation that provides a dense connection between layers that aims to improve the information flow in the network.

85 0

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Unsupervised deep learning for Bayesian brain MRI segmentation

Adrian V. Dalca, M. Sabuncu, P. Golland, Evan M. Yu, B. Fischl, J. E. Iglesias•Wed Apr 24 2019

This paper proposes an alternative strategy that combines a conventional probabilistic atlas-based segmentation with deep learning, enabling one to train a segmentation model for new MRI scans without the need for any manually segmented images.

72 0

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Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

Christian Desrosiers, D. Shen, Jing Yuan, Ismail Ben Ayed, J. Dolz, Li Wang•Wed Dec 13 2017

This work is the first ensemble of 3D CNNs for suggesting annotations within images, and allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net.

119 0

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MRI segmentation | State-of-the-Art