Memory Network-Based Quality Normalization of Magnetic Resonance Images for Brain Segmentation

Yang Su, Jie Wei, Benteng Ma, Yong Xia, Yanning Zhang

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

Abstract

Medical images of the same modality but acquired at different centers, with different machines, using different protocols, and by different operators may have highly variable quality. Due to its limited generalization ability, a deep learning model usually cannot achieve the same performance on another database as it has done on the database with which it was trained. In this paper, we use the segmentation of brain magnetic resonance (MR) images as a case study to investigate the possibility of improving the performance of medical image analysis via normalizing the quality of images. Specifically, we propose a memory network (MemNet)-based algorithm to normalize the quality of brain MR images and adopt the widely used 3D U-Net to segment the images before and after quality normalization. We evaluated the proposed algorithm on the benchmark IBSR V2.0 database. Our results suggest that the MemNet-based algorithm can not only normalize and improve the quality of brain MR images, but also enable the same 3D U-Net to produce substantially more accurate segmentation of major brain tissues.

Original languageEnglish
Title of host publicationIntelligence Science and Big Data Engineering. Visual Data Engineering - 9th International Conference, IScIDE 2019, Proceedings, Part 1
EditorsZhen Cui, Jinshan Pan, Shanshan Zhang, Liang Xiao, Jian Yang
PublisherSpringer
Pages58-67
Number of pages10
ISBN (Print)9783030361884
DOIs
StatePublished - 2019
Event9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019 - Nanjing, China
Duration: 17 Oct 201920 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11935 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019
Country/TerritoryChina
CityNanjing
Period17/10/1920/10/19

Keywords

  • Brain tissue segmentation
  • Deep learning
  • Magnetic resonance image
  • Medical image quality normalization

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