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Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 12963))

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Abstract

Segmentation of brain tumors is a critical task for patient disease management. Since this task is time-consuming and subject to inter-expert delineation variation, automatic methods are of significant interest. The Multimodal Brain Tumor Segmentation Challenge (BraTS) has been in place for about a decade and provides a common platform to compare different automatic segmentation algorithms based on multiparametric magnetic resonance imaging (mpMRI) of gliomas. This year the challenge has taken a big step forward by multiplying the total data by approximately 3. We address the image segmentation challenge by developing a network based on a Bridge-Unet and improved with a concatenation of max and average pooling for downsampling, Squeeze-and-Excitation (SE) block, Atrous Spatial Pyramid Pooling (ASSP), and EvoNorm-S0. Our model was trained using the 1251 training cases from the BraTS 2021 challenge and achieved an average Dice similarity coefficient (DSC) of 0.92457, 0.87811 and 0.84094, as well as a 95% Hausdorff distance (HD) of 4.19442, 7.55256 and 14.13390 mm for the whole tumor, tumor core, and enhanced tumor, respectively on the online validation platform composed of 219 cases. Similarly, our solution achieved a DSC of 0.92548, 0.87628 and 0.87122, as well as HD95 of 4.30711, 17.84987 and 12.23361 mm on the test dataset composed of 530 cases. Overall, our approach yielded well balanced performance for each tumor subregion.

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Acknowledgements

We would like to acknowledge Y. Boursin, M. Deloger, J.P. Larmarque and Gustave Roussy Cancer Campus DTNSI team for providing the infrastructure resources used in this work.

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Authors and Affiliations

  1. Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800, Villejuif, France

    Alexandre Carré, Eric Deutsch & Charlotte Robert

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  1. Alexandre Carré
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  2. Eric Deutsch
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  3. Charlotte Robert
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Correspondence to Charlotte Robert .

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Editors and Affiliations

  1. Sano Centre for Computational Personaliz, Kraków, Poland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Carré, A., Deutsch, E., Robert, C. (2022). Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_23

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  • DOI: https://doi.org/10.1007/978-3-031-09002-8_23

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