Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation

TY - JOUR

T1 - Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation

AU - Wenzel, Fabian

AU - Meyer, Carsten

AU - Stehle, Thomas

AU - Peters, Jochen

AU - Siemonsen, Susanne

AU - Thaler, Christian

AU - Zagorchev, Lyubomir

AU - Alzheimer’s Disease Neuroimaging Initiative

N1 - Copyright © 2018 Elsevier B.V. All rights reserved.

PY - 2018/5

Y1 - 2018/5

N2 - This work presents a novel approach for the rapid segmentation of clinically relevant subcortical brain structures in T1-weighted MRI by utilizing a shape-constrained deformable surface model. In contrast to other approaches for segmenting brain structures, its design allows for parallel segmentation of individual brain structures within a flexible and robust hierarchical framework such that accurate adaptation and volume computation can be achieved within a minute of processing time. Furthermore, adaptation is driven by local and not global contrast, potentially relaxing requirements with respect to preprocessing steps such as bias-field correction. Detailed evaluation experiments on more than 1000 subjects, including comparisons to FSL FIRST and FreeSurfer as well as a clinical assessment, demonstrate high accuracy and test-retest consistency of the presented segmentation approach, leading, for example, to an average segmentation error of less than 0.5 mm. The presented approach might be useful in both, research as well as clinical routine, for automated segmentation and volume quantification of subcortical brain structures in order to increase confidence in the diagnosis of neuro-degenerative disorders, such as Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, or clinical applications for other neurologic and psychiatric diseases.

AB - This work presents a novel approach for the rapid segmentation of clinically relevant subcortical brain structures in T1-weighted MRI by utilizing a shape-constrained deformable surface model. In contrast to other approaches for segmenting brain structures, its design allows for parallel segmentation of individual brain structures within a flexible and robust hierarchical framework such that accurate adaptation and volume computation can be achieved within a minute of processing time. Furthermore, adaptation is driven by local and not global contrast, potentially relaxing requirements with respect to preprocessing steps such as bias-field correction. Detailed evaluation experiments on more than 1000 subjects, including comparisons to FSL FIRST and FreeSurfer as well as a clinical assessment, demonstrate high accuracy and test-retest consistency of the presented segmentation approach, leading, for example, to an average segmentation error of less than 0.5 mm. The presented approach might be useful in both, research as well as clinical routine, for automated segmentation and volume quantification of subcortical brain structures in order to increase confidence in the diagnosis of neuro-degenerative disorders, such as Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, or clinical applications for other neurologic and psychiatric diseases.

KW - Journal Article

KW - Research Support, N.I.H., Extramural

KW - Research Support, U.S. Gov't, Non-P.H.S.

KW - Research Support, Non-U.S. Gov't

U2 - 10.1016/j.media.2018.03.001

DO - 10.1016/j.media.2018.03.001

M3 - SCORING: Journal article

C2 - 29550581

VL - 46

SP - 146

EP - 161

JO - MED IMAGE ANAL

JF - MED IMAGE ANAL

SN - 1361-8415

ER -