One-Pass Multi-task Convolutional Neural Networks for Efficient Brain Tumor Segmentation

Chenhong Zhou, Changxing Ding*, Zhentai Lu, Xinchao Wang, Dacheng Tao

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

39 Citations (Scopus)

Abstract

The model cascade strategy that runs a series of deep models sequentially for coarse-to-fine medical image segmentation is becoming increasingly popular, as it effectively relieves the class imbalance problem. This strategy has achieved state-of-the-art performance in many segmentation applications but results in undesired system complexity and ignores correlation among deep models. In this paper, we propose a light and clean deep model that conducts brain tumor segmentation in a single-pass and solves the class imbalance problem better than model cascade. First, we decompose brain tumor segmentation into three different but related tasks and propose a multi-task deep model that trains them together to exploit their underlying correlation. Second, we design a curriculum learning-based training strategy that trains the above multi-task model more effectively. Third, we introduce a simple yet effective post-processing method that can further improve the segmentation performance significantly. The proposed methods are extensively evaluated on BRATS 2017 and BRATS 2015 datasets, ranking first on the BRATS 2015 test set and showing top performance among 60+ competing teams on the BRATS 2017 validation set.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018
Subtitle of host publication21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part III
EditorsAlejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger
PublisherSpringer Cham
Pages637-645
Number of pages9
Edition1st
ISBN (Electronic)9783030009311
ISBN (Print)9783030009304
DOIs
Publication statusPublished - 13 Sep 2018
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 16 Sep 201820 Sep 2018

Publication series

NameLecture Notes in Computer Science
Volume11072
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameImage Processing, Computer Vision, Pattern Recognition, and Graphics
NameMICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention

Conference

Conference21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/TerritorySpain
CityGranada
Period16/09/1820/09/18

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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