A multi-path decoder network for brain tumor segmentation

Yunzhe Xue, Meiyan Xie, Fadi G. Farhat, Olga Boukrina, A. M. Barrett, Jeffrey R. Binder, Usman W. Roshan, William W. Graves

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

1 Scopus citations

Abstract

The identification of brain tumor type, shape, and size from MRI images plays an important role in glioma diagnosis and treatment. Manually identifying the tumor is time expensive and prone to error. And while information from different image modalities may help in principle, using these modalities for manual tumor segmentation may be even more time consuming. Convolutional U-Net architectures with encoders and decoders are state of the art in automated methods for image segmentation. Often only a single encoder and decoder is used, where different modalities and regions of the tumor share the same model parameters. This may lead to incorrect segmentations. We propose a convolutional U-Net that has separate, independent encoders for each image modality. The outputs from each encoder are concatenated and given to separate fusion and decoder blocks for each region of the tumor. The features from each decoder block are then calibrated in a final feature fusion block, after which the model gives it final predictions. Our network is an end-to-end model that simplifies training and reproducibility. On the BraTS 2019 validation dataset our model achieves average Dice values of 0.75, 0.90, and 0.83 for the enhancing tumor, whole tumor, and tumor core subregions respectively.

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
Pages255-265
Number of pages11
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
Country/TerritoryChina
CityShenzhen
Period10/17/1910/17/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Brain MRI
  • Convolutional neural networks
  • Multi-modal

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