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2018 | OriginalPaper | Chapter

Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset

Authors : Bo Li, Marius de Groot, Meike W. Vernooij, M. Arfan Ikram, Wiro J. Niessen, Esther E. Bron

Published in: Machine Learning in Medical Imaging

Publisher: Springer International Publishing

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Abstract

Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract segmentation. However, because of the limited consistency and long processing time, such methods may not be suitable for clinical practice. We therefore developed a novel convolutional neural network based method to directly segment white matter tract trained on a low-resolution dataset of 9149 DTI images. The method is optimized on input, loss function and network architecture selections. We evaluated both segmentation accuracy and reproducibility, and reproducibility of determining tract-specific diffusion measures. The reproducibility of the method is higher than that of the reference standard and the determined diffusion measures are consistent. Therefore, we expect our method to be applicable in clinical practice and in longitudinal analysis of white matter microstructure.
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Metadata
Title
Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset
Authors
Bo Li
Marius de Groot
Meike W. Vernooij
M. Arfan Ikram
Wiro J. Niessen
Esther E. Bron
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-00919-9_24

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