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 proceedingChapter

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 ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages759-767
Number of pages9
Volume16
EditionPt 1
StatePublished - 2013
Externally publishedYes

Fingerprint

Atlases
Spinal Cord
Magnetic Resonance Imaging
Benchmarking
Artifacts
Noise
Central Nervous System

ASJC Scopus subject areas

  • Medicine(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 ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 16, pp. 759-767)

Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion. / Asman, Andrew J.; Smith, Seth A.; Reich, Daniel S.; Landman, Bennett A.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 1. ed. 2013. p. 759-767.

Research output: Chapter in Book/Report/Conference proceedingChapter

Asman, AJ, Smith, SA, Reich, DS & Landman, BA 2013, Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 edn, vol. 16, pp. 759-767.
Asman AJ, Smith SA, Reich DS, Landman BA. Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 ed. Vol. 16. 2013. p. 759-767
Asman, Andrew J. ; Smith, Seth A. ; Reich, Daniel S. ; Landman, Bennett A. / Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 1. ed. 2013. pp. 759-767
@inbook{9f54112237ed436a8b97d030310a080e,
title = "Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion.",
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.",
author = "Asman, {Andrew J.} and Smith, {Seth A.} and Reich, {Daniel S.} and Landman, {Bennett A.}",
year = "2013",
language = "English (US)",
volume = "16",
pages = "759--767",
booktitle = "Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention",
edition = "Pt 1",

}

TY - CHAP

T1 - Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion.

AU - Asman, Andrew J.

AU - Smith, Seth A.

AU - Reich, Daniel S.

AU - Landman, Bennett A.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84894640133&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84894640133&partnerID=8YFLogxK

M3 - Chapter

C2 - 24505736

AN - SCOPUS:84894640133

VL - 16

SP - 759

EP - 767

BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

ER -