Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion

Andrew J. Asman, Seth A. Smith, Daniel S. Reich, Bennett A. Landman

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

Abstract

New magnetic resonance imaging (MRI) sequences are enabling clinical study of the in vivo spinal cord's internal structure. Yet, low contrast-to-noise ratio, artifacts, and imaging distortions have limited the applicability of tissue segmentation techniques pioneered elsewhere in the central nervous system. Recently, methods have been presented for cord/non-cord segmentation on MRI and the feasibility of gray matter/white matter tissue segmentation has been evaluated. To date, no automated algorithms have been presented. Herein, we present a non-local multi-atlas framework that robustly identifies the spinal cord and segments its internal structure with submillimetric accuracy. The proposed algorithm couples non-local fusion with a large number of slice-based atlases (as opposed to typical volumetric ones). To improve performance, the fusion process is interwoven with registration so that segmentation information guides registration and vice versa. We demonstrate statistically significant improvement over state-of-the-art benchmarks in a study of 67 patients. The primary contributions of this work are (1) innovation in non-volumetric atlas information, (2) advancement of label fusion theory to include iterative registration/segmentation, and (3) the first fully automated segmentation algorithm for spinal cord internal structure on MRI.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages759-767
Number of pages9
EditionPART 1
DOIs
StatePublished - Oct 23 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 26 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8149 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/26/13

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Keywords

  • Multi-Atlas Segmentation
  • Non-local Correspondence Models
  • Registration Refinement
  • Spinal Cord Parcellation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Asman, A. J., Smith, S. A., Reich, D. S., & Landman, B. A. (2013). Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 1 ed., pp. 759-767). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8149 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-40811-3_95