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Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database

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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

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. This is the 10th year of Brain Tumor Segmentation (BraTS) Challenge that utilizes multi-institutional multi-parametric magnetic resonance imaging (mpMRI) scans for tasks: 1) evaluation the state-of-the-art methods for the segmentation of intrinsically heterogeneous brain glioblastoma sub-regions in mpMRI scans; and 2) the evaluation of classification methods to predict the MGMT promoter methylation status at pre-operative baseline scans. We participated the image segmentation task by applying a fully automated segmentation framework that we previously developed in BraTS 2020. This framework, named as scale-attention network, incorporates a dynamic scale attention mechanism to integrate low-level details with high-level feature maps at different scales. Our framework was trained using the 1251 challenge training cases provided by BraTS 2021, and achieved an average Dice Similarity Coefficient (DSC) of 0.9277, 0.8851 and 0.8754, as well as \(95\%\) Hausdorff distance (in millimeter) of 4.2242, 15.3981 and 11.6925 on 570 testing cases for whole tumor, tumor core and enhanced tumor, respectively, which ranked itself as the second place in the brain tumor segmentation task of RSNA-ASNR-MICCAI BraTS 2021 Challenge (id: deepX).

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Acknowledgment

This work is supported by a research grant from Varian Medical Systems (Palo Alto, CA, USA), UL1TR001433 from the National Center for Advancing Translational Sciences, and R21EB030209 from the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, USA. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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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. Sano Centre for Computational Personaliz, Kraków, Poland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Yuan, Y. (2022). Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database. 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_4

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  • DOI: https://doi.org/10.1007/978-3-031-09002-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09001-1

  • Online ISBN: 978-3-031-09002-8

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