Whole brain segmentation and labeling from CT using synthetic MR images

Can Zhao, Aaron Carass, Junghoon Lee, Yufan He, Jerry Ladd Prince

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

Abstract

To achieve whole-brain segmentation—i.e., classifying tissues within and immediately around the brain as gray matter (GM), white matter (WM), and cerebrospinal fluid—magnetic resonance (MR) imaging is nearly always used. However, there are many clinical scenarios where computed tomography (CT) is the only modality that is acquired and yet whole brain segmentation (and labeling) is desired. This is a very challenging task, primarily because CT has poor soft tissue contrast; very few segmentation methods have been reported to date and there are no reports on automatic labeling. This paper presents a whole brain segmentation and labeling method for non-contrast CT images that first uses a fully convolutional network (FCN) to synthesize an MR image from a CT image and then uses the synthetic MR image in a standard pipeline for whole brain segmentation and labeling. The FCN was trained on image patches derived from ten co-registered MR and CT images and the segmentation and labeling method was tested on sixteen CT scans in which co-registered MR images are available for performance evaluation. Results show excellent MR image synthesis from CT images and improved soft tissue segmentation and labeling over a multi-atlas segmentation approach.

Original languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages291-298
Number of pages8
ISBN (Print)9783319673882
DOIs
StatePublished - Jan 1 2017
Event8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: Sep 10 2017Sep 10 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10541 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period9/10/179/10/17

Keywords

  • CNN
  • CT
  • Deep learning
  • FCN U-net
  • MR
  • Segmentation
  • Synthesis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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