Dataset Information

Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation.

ABSTRACT: Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology.

SUBMITTER: Bastiani M

PROVIDER: S-EPMC5102896 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using <i>Ex vivo</i> Diffusion MRI and Its Validation.

Bastiani Matteo M Oros-Peusquens Ana-Maria AM Seehaus Arne A Brenner Daniel D Möllenhoff Klaus K Celik Avdo A Felder Jörg J Bratzke Hansjürgen H Shah Nadim J NJ Galuske Ralf R Goebel Rainer R Roebroeck Alard A

Frontiers in neuroscience 20161110

Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect a ...[more]

PMID: 27891069

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Project description:BackgroundSegmentation of medical image volumes is a time-consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings.PurposeTo evaluate the performance of the open-source Multi-Planar U-Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state-of-the-art two-dimensional (2D) U-Net architecture on three clinical cohorts without extensive adaptation of the algorithms.Study typeRetrospective cohort study.SubjectsA total of 253 subjects (146 females, 107 males, ages 57 ± 12 years) from three knee osteoarthritis (OA) studies (Center for Clinical and Basic Research [CCBR], Osteoarthritis Initiative [OAI], and Prevention of OA in Overweight Females [PROOF]) with varying demographics and OA severity (64/37/24/53/2 scans of Kellgren and Lawrence [KL] grades 0-4).Field strength/sequence0.18 T, 1.0 T/1.5 T, and 3 T sagittal three-dimensional fast-spin echo T1w and dual-echo steady-state sequences.AssessmentAll models were fit without tuning to knee magnetic resonance imaging (MRI) scans with manual segmentations from three clinical cohorts. All models were evaluated across KL grades.Statistical testsSegmentation performance differences as measured by Dice coefficients were tested with paired, two-sided Wilcoxon signed-rank statistics with significance threshold α = 0.05.ResultsThe MPUnet performed superior or equal to KIQ and 2D U-Net on all compartments across three cohorts. Mean Dice overlap was significantly higher for MPUnet compared to KIQ and U-Net on CCBR ( 0.83±0.04 vs. 0.81±0.06 and 0.82±0.05 ), significantly higher than KIQ and U-Net OAI ( 0.86±0.03 vs. 0.84±0.04 and 0.85±0.03) , and not significantly different from KIQ while significantly higher than 2D U-Net on PROOF ( 0.78±0.07 vs. 0.77±0.07 , P=0.10 , and 0.73±0.07) . The MPUnet performed significantly better on N=22 KL grade 3 CCBR scans with 0.78±0.06 vs. 0.75±0.08 for KIQ and 0.76±0.06 for 2D U-Net.Data conclusionThe MPUnet matched or exceeded the performance of state-of-the-art knee MRI segmentation models across cohorts of variable sequences and patient demographics. The MPUnet required no manual tuning making it both accurate and easy-to-use.Level of evidence3 TECHNICAL EFFICACY: Stage 2.

Dataset Information

Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation.

Publications

Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using <i>Ex vivo</i> Diffusion MRI and Its Validation.

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