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Automatic Brain Tumor Segmentation with Scale Attention Network

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12658))

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Abstract

Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment. Multimodal Brain Tumor Segmentation Challenge 2020 (BraTS 2020) provides a common platform for comparing different automatic algorithms on multi-parametric Magnetic Resonance Imaging (mpMRI) in tasks of 1) Brain tumor segmentation MRI scans; 2) Prediction of patient overall survival (OS) from pre-operative MRI scans; 3) Distinction of true tumor recurrence from treatment related effects and 4) Evaluation of uncertainty measures in segmentation. We participate the image segmentation challenge by developing a fully automatic segmentation network based on encoder-decoder architecture. In order to better integrate information across different scales, we propose a dynamic scale attention mechanism that incorporates low-level details with high-level semantics from feature maps at different scales. Our framework was trained using the 369 challenge training cases provided by BraTS 2020, and achieved an average Dice Similarity Coefficient (DSC) of 0.8828, 0.8433 and 0.8177, as well as \(95\%\) Hausdorff distance (in millimeter) of 5.2176, 17.9697 and 13.4298 on 166 testing cases for whole tumor, tumor core and enhanced tumor, respectively, which ranked itself as the 3rd place among 693 registrations in the BraTS 2020 challenge.

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Acknowledgment

This work is partially supported by a research grant from Varian Medical Systems (Palo Alto, CA, USA) and grant UL1TR001433 from the National Center for Advancing Translational Sciences, National Institutes of Health, USA.

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Authors and Affiliations

  1. Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Yading Yuan

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  1. Yading Yuan
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Correspondence to Yading Yuan .

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Editors and Affiliations

  1. University of Zurich, Zurich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Yuan, Y. (2021). Automatic Brain Tumor Segmentation with Scale Attention Network. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_26

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  • DOI: https://doi.org/10.1007/978-3-030-72084-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72083-4

  • Online ISBN: 978-3-030-72084-1

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