mlsanityData to 7 Oct 2026

Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge

arXiv 2109.05409 ↗First posted 12 Sep 2021eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

This paper gives a detailed description of the pipelines used for the 2nd edition of the MICCAI 2021 Challenge on Multiple Sclerosis Lesion Segmentation. An overview of the data preprocessing steps applied is provided along with a brief description of the pipelines used, in terms of the architecture and the hyperparameters. Our code for this work can be found at: https://github.com/ivadomed/ms-challenge-2021.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 12 Sep 2021.

Builds on

13 of 21 references.

References · 13

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2013Auto-Encoding Variational Bayescited by 3,980
  4. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  5. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  6. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  7. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  8. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 281
  9. 2020TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learningcited by 176
  10. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  11. 2020SoftSeg: Advantages of soft versus binary training for image segmentationcited by 26
  12. 2018Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networkscited by 14
1 more
  1. 2020ivadomed: A Medical Imaging Deep Learning Toolboxcited by 9

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentationcited by 9