Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework

David G. Ellis, Michele R. Aizenberg

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

1 Scopus citations

Abstract

Automatic brain segmentation has the potential to save time and resources for researchers and clinicians. We aimed to improve upon previously proposed methods by implementing the U-Net model and trialing various modifications to the training and inference strategies. The trials were performed and tested on the Multimodal Brain Tumor Segmentation dataset that provides MR images of brain tumors along with manual segmentations for hundreds of subjects. The U-Net models were trained on a training set of MR images from 369 subjects and then tested against a validation set of images from 125 subjects. The proposed modifications included predicting the labeled region contours, permutations of the input data via rotation and reflection, grouping labels together, as well as creating an ensemble of models. The ensemble of models provided the best results compared to any of the other methods, but the other modifications did not demonstrate improvement. Future work will look at reducing the level of the training augmentation so that the models are better able to generalize to the validation set. Overall, our open source deep learning framework allowed us to quickly implement and test multiple U-Net training modifications. The code for this project is available at https://github.com/ellisdg/3DUnetCNN.

Original languageEnglish (US)
Title of host publicationBrainlesion
Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
EditorsAlessandro Crimi, Spyridon Bakas
PublisherSpringer Science and Business Media Deutschland GmbH
Pages40-49
Number of pages10
ISBN (Print)9783030720865
DOIs
StatePublished - 2021
Event6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020 - Virtual, Online
Duration: Oct 4 2020Oct 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12659 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020
CityVirtual, Online
Period10/4/2010/4/20

Keywords

  • Brain tumor segmentation
  • Deep learning
  • U-Net

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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