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Disparity Autoencoders for Multi-class Brain Tumor Segmentation

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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

Multi-class brain tumor segmentation is important for predicting the aggressiveness and treatment response of gliomas. It has various applications including diagnosis, monitoring, and treatment planning of gliomas. The purpose of this work was to develop a fully automated deep learning framework for multi-class brain tumor segmentation. Brain tumor cases with multi-parametric MR Images from the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021 were used. Six Disparity Autoencoders (DAE) were developed including 2 DAEs to segment the whole-tumor (WT), 2 DAEs to segment the tumor-core (TC) and 2 DAEs to segment the enhancing-tumor (ET). The output segmentations of a particular label from their respective DAEs were ensembled and post-processed. The DAEs were tested on the BraTS2021 validation dataset. The networks achieved average dice-scores of 0.90, 0.80 and 0.79 for WT, TC and ET respectively on the validation dataset and 0.89, 0.82, 0.81 for WT, TC and ET respectively on the test dataset. This framework could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.

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Acknowledgement

Support for this research was provided by NCI U01CA207091 (AJM, JAM) and NCI R01CA260705 (JAM).

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Authors and Affiliations

  1. Advanced Neuroscience Imaging Research Lab, Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Chandan Ganesh Bangalore Yogananda, Yudhajit Das, Benjamin C. Wagner, Sahil S. Nalawade, Divya Reddy, James Holcomb, Marco C. Pinho, Ananth J. Madhuranthakam & Joseph A. Maldjian

  2. Department of Bioengineering, University of Texas at Dallas, Dallas, TX, USA

    Baowei Fei

Authors
  1. Chandan Ganesh Bangalore Yogananda
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  2. Yudhajit Das
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  3. Benjamin C. Wagner
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  4. Sahil S. Nalawade
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  6. James Holcomb
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  7. Marco C. Pinho
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  8. Baowei Fei
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  9. Ananth J. Madhuranthakam
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  10. Joseph A. Maldjian
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Correspondence to Chandan Ganesh Bangalore Yogananda .

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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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Bangalore Yogananda, C.G. et al. (2022). Disparity Autoencoders for Multi-class Brain Tumor Segmentation. 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_11

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  • DOI: https://doi.org/10.1007/978-3-031-09002-8_11

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  • Online ISBN: 978-3-031-09002-8

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