LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain

Ipek Oguz; Milan Sonka

doi:10.1109/TMI.2014.2304499

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LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain

Journal article

Peer reviewed

LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain

Ipek Oguz and Milan Sonka

IEEE transactions on medical imaging, Vol.33(6), pp.1220-1235

06/2014

DOI: 10.1109/TMI.2014.2304499

PMCID: PMC4324764

PMID: 24760901

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Abstract

Automated reconstruction of the cortical surface is one of the most challenging problems in the analysis of human brain magnetic resonance imaging (MRI). A desirable segmentation must be both spatially and topologically accurate, as well as robust and computationally efficient. We propose a novel algorithm, LOGISMOS-B, based on probabilistic tissue classification, generalized gradient vector flows and the LOGISMOS graph segmentation framework. Quantitative results on MRI datasets from both healthy subjects and multiple sclerosis patients using a total of 16 800 manually placed landmarks illustrate the excellent performance of our algorithm with respect to spatial accuracy. Remarkably, the average signed error was only 0.084 mm for the white matter and 0.008 mm for the gray matter, even in the presence of multiple sclerosis lesions. Statistical comparison shows that LO-GISMOS-B produces a significantly more accurate cortical reconstruction than FreeSurfer, the current state-of-the-art approach ( p ≪ 0.001. Furthermore, LOGISMOS-B enjoys a run time that is less than a third of that of FreeSurfer, which is both substantial, considering the latter takes 10 h/subject on average, and a statistically significant speedup.

Cortical reconstruction

generalized gradient vector flow

LOGISMOS

multi-layered graph search

optimal multi-surface segmentation

Details

Title: Subtitle: LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain
Creators: Ipek Oguz - University of Iowa
Milan Sonka - University of Iowa
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.33(6), pp.1220-1235
DOI: 10.1109/TMI.2014.2304499
PMID: 24760901
PMCID: PMC4324764
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Language: English
Date published: 06/2014
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984186699402771

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