Reproducible white matter tract segmentation using 3D U-net on a large-scale DTI dataset

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

*Corresponding author for this work

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    6 Citations (Scopus)

    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.

    Original languageEnglish
    Title of host publicationMachine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
    EditorsYinghuan Shi, Heung-Il Suk, Mingxia Liu
    PublisherSpringer
    Pages205-213
    Volume11046 LNCS
    ISBN (Print)9783030009182
    DOIs
    Publication statusPublished - 2018
    Event9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
    Duration: 16 Sept 201816 Sept 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11046 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
    Country/TerritorySpain
    CityGranada
    Period16/09/1816/09/18

    Keywords

    • 3D
    • Convolution neural network
    • Diffusion measurements
    • DTI
    • Low resolution
    • Segmentation
    • Tract
    • White Matter

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