Disparity Autoencoders for Multi-class Brain Tumor Segmentation

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationBrainlesion
Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers
EditorsAlessandro Crimi, Spyridon Bakas
PublisherSpringer Science and Business Media Deutschland GmbH
Pages116-124
Number of pages9
ISBN (Print)9783031090011
DOIs
StatePublished - 2022
Event7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: Sep 27 2021Sep 27 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12963 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period9/27/219/27/21

Keywords

  • Autoencoders
  • Brain tumor segmentation
  • BraTS
  • Deep learning
  • Glioma segmentation
  • Imaging features
  • MRI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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