Article

Scalable Multimodal Convolutional Networks for Brain Tumour Segmentation

Authors:

Lucas Fidon,

Wenqi Li,

Luis C. Garcia-Peraza-Herrera,

Jinendra Ekanayake,

Neil Kitchen,

Sebastien Ourselin,

Tom VercauterenAuthors Info & Claims

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III

Pages 285 - 293

https://doi.org/10.1007/978-3-319-66179-7_33

Published: 10 September 2017 Publication History

Abstract

Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.

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Cited By

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Fan Chiang YLi PWu DHsieh HKo C(2023)Exploring Feature Fusion from A Contrastive Multi-Modality Learner for Liver Cancer DiagnosisProceedings of the 5th ACM International Conference on Multimedia in Asia10.1145/3595916.3626383(1-7)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3595916.3626383
Győrfi ÁKaretka-Mezei ZIclănzan DKovács LSzilágyi L(2019)A Study on Histogram Normalization for Brain Tumour Segmentation from Multispectral MR Image DataProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-33904-3_35(375-384)Online publication date: 28-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-33904-3_35
Chen CDou QJin YChen HQin JHeng P(2019)Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated FusionMedical Image Computing and Computer Assisted Intervention – MICCAI 201910.1007/978-3-030-32248-9_50(447-456)Online publication date: 13-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-32248-9_50
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Index Terms

Scalable Multimodal Convolutional Networks for Brain Tumour Segmentation
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III

Sep 2017

696 pages

ISBN:978-3-319-66178-0

DOI:10.1007/978-3-319-66179-7

Editors:
Maxime Descoteaux
Université de Sherbrooke, Sherbrooke, QC, Canada
,
Lena Maier-Hein
DKFZ, Heidelberg, Germany
,
Alfred Franz
Ulm University of Applied Sciences, Ulm, Germany
,
Pierre Jannin
Université de Rennes 1, Rennes, France
,
D. Louis Collins
McGill University, Montreal, QC, Canada
,
Simon Duchesne
Université Laval, Québec, QC, Canada

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Springer-Verlag

Berlin, Heidelberg

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Published: 10 September 2017

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Fan Chiang YLi PWu DHsieh HKo C(2023)Exploring Feature Fusion from A Contrastive Multi-Modality Learner for Liver Cancer DiagnosisProceedings of the 5th ACM International Conference on Multimedia in Asia10.1145/3595916.3626383(1-7)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3595916.3626383
Győrfi ÁKaretka-Mezei ZIclănzan DKovács LSzilágyi L(2019)A Study on Histogram Normalization for Brain Tumour Segmentation from Multispectral MR Image DataProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-33904-3_35(375-384)Online publication date: 28-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-33904-3_35
Chen CDou QJin YChen HQin JHeng P(2019)Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated FusionMedical Image Computing and Computer Assisted Intervention – MICCAI 201910.1007/978-3-030-32248-9_50(447-456)Online publication date: 13-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-32248-9_50
González GWashko GSan José Estépar R(2018)Multi-structure Segmentation from Partially Labeled Datasets. Application to Body Composition Measurements on CT ScansImage Analysis for Moving Organ, Breast, and Thoracic Images10.1007/978-3-030-00946-5_22(215-224)Online publication date: 16-Aug-2018
https://dl.acm.org/doi/10.1007/978-3-030-00946-5_22

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Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021

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