medical-1

Brain Image Segmentation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in brain-image-segmentation-3

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FIB-25 Whole Test

FIB-25 Synaptic Sites

CREMI

Libraries

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Use these libraries to find brain-image-segmentation-3 models and implementations

Datasets

CREMI

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Most implemented papers

FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics

Tran Minh Quan, David Grant Colburn Hildebrand, W. Jeong•Thu Dec 15 2016

A deep neural network architecture, FusionNet, is introduced with a focus on its application to accomplish automatic segmentation of neuronal structures in connectomics data, which results in a much deeper network architecture and improves segmentation accuracy.

261

Content

CREMI

SegEM

Brain Tumor

T1-weighted MRI

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Non-local U-Net for Biomedical Image Segmentation

Zhengyang Wang, Na Zou, Dinggang Shen, Shuiwang Ji•Sun Dec 09 2018

This work proposes the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation, and shows that the proposed models achieve top performances with fewer parameters and faster computation.

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Enforcing temporal consistency in Deep Learning segmentation of brain MR images

Malav Bateriwala, P. Bourgeat•Wed Jun 12 2019

Proposed CNN based segmentation approaches demonstrate how 2D segmentation using prior slices can provide similar results to 3D segmentations while maintaining good continuity in the 3D dimension and improved speed.

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NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

D. Rueckert, Ben Glocker, Nick Pawlowski, Martin Rajchl, P. Matthews•Tue Apr 10 2018

The network is trained on 5,000 T1-weighted brain MRI scans from the UK Biobank Imaging Study that have been automatically segmented into brain tissue and cortical and sub-cortical structures using the standard neuroimaging pipelines and demonstrates very good reproducibility of the original outputs while increasing robustness to variations in the input data.

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Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

Christian Desrosiers, J. Dolz, Arnab Kumar Mondal•Sun Oct 28 2018

This work proposes a novel method based on Generative Adversarial Networks (GANs) to train a segmentation model with both labeled and unlabeled images, which prevents over-fitting by learning to discriminate between true and fake patches obtained by a generator network.

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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.

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ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging

Shadi Albarqouni, N. Navab, T. Bdair•Thu Mar 19 2020

ROAM is proposed, a \textit{random layer mixup}, which encourages the network to be less confident for interpolated data points at randomly selected space, and hence it avoids over-fitting and enhances the generalization ability.

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Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation

J. Dolz, Yang Ding, Rolando Acosta, Vicente Enguix, Sabrina Suffren, Janosch Ortmann, D. Luck, G. Lodygensky•Wed Mar 25 2020

The evaluation demonstrates that both neural networks can segment neonatal brains, achieving previously reported performance and will be continuously retrained over an increasingly larger repertoire of neonatal brain data and be made available through the Canadian Neonatal Brain Platform.

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A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis

K. V. Leemput, Stefano Cerri, O. Puonti, D. Meier, J. Wuerfel, M. Mühlau, H. Siebner•Sun May 10 2020

A novel model for white matter lesions into a previously validated generative model for whole-brain segmentation is integrated, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures from multi-contrast brain MRI scans of multiple sclerosis patients.

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A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

Andrew Hoopes, D. Greve, K. V. Leemput, Stefano Cerri, Mark Muhlau•Tue Aug 11 2020

Preliminary experiments on three longitudinal datasets indicate that the proposed method produces more reliable segmentations and detects disease effects better than the cross-sectional method it is based upon.

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