Multidimensional and multiresolution ensemble networks for brain tumor segmentation

Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian

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

1 Scopus citations

Abstract

In this work, we developed multiple 2D and 3D segmentation models with multiresolution input to segment brain tumor components and then ensembled them to obtain robust segmentation maps. Ensembling reduced overfitting and resulted in a more generalized model. Multiparametric MR images of 335 subjects from the BRATS 2019 challenge were used for training the models. Further, we tested a classical machine learning algorithm with features extracted from the segmentation maps to classify subject survival range. Preliminary results on the BRATS 2019 validation dataset demonstrated excellent performance with DICE scores of 0.898, 0.784, 0.779 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively and an accuracy of 34.5% for predicting survival. The Ensemble of multiresolution 2D networks achieved 88.75%, 83.28% and 79.34% dice for WT, TC, and ET respectively in a test dataset of 166 subjects.

Original languageEnglish (US)
Title of host publicationBrainlesion
Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers
EditorsAlessandro Crimi, Spyridon Bakas
PublisherSpringer
Pages148-157
Number of pages10
ISBN (Print)9783030466428
DOIs
StatePublished - 2020
Event5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 17 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11993 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/17/1910/17/19

Keywords

  • Brain tumor segmentation
  • Densenet-169
  • Residual Inception Dense Networks
  • Squeezenet
  • Survival prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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