Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation
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Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation. / Wenzel, Fabian; Meyer, Carsten; Stehle, Thomas; Peters, Jochen; Siemonsen, Susanne; Thaler, Christian; Zagorchev, Lyubomir; Alzheimer’s Disease Neuroimaging Initiative.
in: MED IMAGE ANAL, Jahrgang 46, 05.2018, S. 146-161.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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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 -