Dataset Information

Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions.

ABSTRACT: We present a deep learning tractography segmentation method that allows fast and consistent white matter fiber tract identification across healthy and disease populations and across multiple diffusion MRI (dMRI) acquisitions. We create a large-scale training tractography dataset of 1 million labeled fiber samples (54 anatomical tracts are included). To discriminate between fibers from different tracts, we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a CNN tract classification model based on FiberMap and obtain a high tract classification accuracy of 90.99%. The method is evaluated on a test dataset of 374 dMRI scans from three independently acquired populations across health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art white matter tract segmentation methods. Experimental results show that our method obtains a highly consistent segmentation result, where over 99% of the fiber tracts are successfully detected across all subjects under study, most importantly, including patients with space occupying brain tumors. The proposed method leverages deep learning techniques and provides a much faster and more efficient tool for large data analysis than methods using traditional machine learning techniques.

SUBMITTER: Zhang F

PROVIDER: S-EPMC7301958 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions.

Zhang Fan F Hoffmann Nico N Karayumak Suheyla Cetin SC Rathi Yogesh Y Golby Alexandra J AJ O'Donnell Lauren J LJ

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 20191010

We present a deep learning tractography segmentation method that allows fast and consistent white matter fiber tract identification across healthy and disease populations and across multiple diffusion MRI (dMRI) acquisitions. We create a large-scale training tractography dataset of 1 million labeled fiber samples (54 anatomical tracts are included). To discriminate between fibers from different tracts, we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordin ...[more]

PMID: 32558816

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Dataset Information

Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions.

Publications

Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions.

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