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Brain Tumor Segmentation in mpMRI Scans (BraTS-2021) Using Models Based on U-Net Architecture

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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

Accurate segmentation of brain tumors from MR images has important clinical relevance. To overcome the limitations of manual segmentation, a semi-automatic or automatic approach is desirable. The current study mainly focused on task-1 defined in the BraTS'21 challenge, i.e., segmenting glioblastoma into sub-regions (the “enhancing tumor” (ET), the “tumor core” (TC), and the “whole tumor” (WT)). In the current study, deep learning models based upon UNet architecture were developed for producing tumor segmentation labels from 3D multi-parametric MRI (mpMRI) scans. During the segmentation process, the output mask resulting from the first developed Model (WT) was used to develop segmentation models for the remaining subregions. Optimizations were carried out to develop robust models, reduce computation time, and achieve high accuracy. Developed models showed high accuracy for the segmentation of tumor sub-regions on training as well as validation data.

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Acknowledgments

The authors acknowledge funding support from the Science and Engineering Research Board, Department of Science and Technology project: CRG/2019/005032.

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Authors and Affiliations

  1. Indian Institute of Technology, Delhi, India

    Satyajit Maurya, Virendra Kumar Yadav, Sumeet Agarwal & Anup Singh

  2. School of Artificial Intelligence, Indian Institute of Technology, Delhi, India

    Sumeet Agarwal & Anup Singh

  3. All India Institute of Medical Science, Delhi, India

    Anup Singh

Authors
  1. Satyajit Maurya
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  2. Virendra Kumar Yadav
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  3. Sumeet Agarwal
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  4. Anup Singh
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Correspondence to Anup Singh .

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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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Maurya, S., Kumar Yadav, V., Agarwal, S., Singh, A. (2022). Brain Tumor Segmentation in mpMRI Scans (BraTS-2021) Using Models Based on U-Net Architecture. 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_28

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  • DOI: https://doi.org/10.1007/978-3-031-09002-8_28

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  • Online ISBN: 978-3-031-09002-8

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