Deep learning based multimodal brain tumor diagnosis

Yuexiang Li, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

18 Citations (Scopus)

Abstract

Brain tumor segmentation plays an important role in the disease diagnosis. In this paper, we proposed deep learning frameworks, i.e. MvNet and SPNet, to address the challenges of multimodal brain tumor segmentation. The proposed multi-view deep learning framework (MvNet) uses three multi-branch fully-convolutional residual networks (Mb-FCRN) to segment multimodal brain images from different view-point, i.e. slices along x, y, z axis. The three sub-networks produce independent segmentation results and vote for the final outcome. The SPNet is a CNN-based framework developed to predict the survival time of patients. The proposed deep learning frameworks was evaluated on BraTS 17 validation set and achieved competing results for tumor segmentation While Dice scores of 0.88, 0.75 0.71 were achieved for whole tumor, enhancing tumor and tumor core, respectively, an accuracy of 0.55 was obtained for survival prediction.

Original languageEnglish
Title of host publicationBrainlesion
Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 3rd International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Revised Selected Papers
EditorsBjoern Menze, Alessandro Crimi, Hugo Kuijf, Mauricio Reyes, Spyridon Bakas
PublisherSpringer Verlag
Pages149-158
Number of pages10
ISBN (Print)9783319752372
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event3rd International Workshop on Brainlesion, BrainLes 2017 Held in Conjunction with Medical Image Computing for Computer Assisted Intervention , MICCAI 2017 - Quebec City, Canada
Duration: 14 Sep 201714 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10670 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Brainlesion, BrainLes 2017 Held in Conjunction with Medical Image Computing for Computer Assisted Intervention , MICCAI 2017
Country/TerritoryCanada
CityQuebec City
Period14/09/1714/09/17

Keywords

  • Deep learning
  • Multi-view
  • Survival prediction
  • Tumor segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

Fingerprint

Dive into the research topics of 'Deep learning based multimodal brain tumor diagnosis'. Together they form a unique fingerprint.

Cite this