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Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI

Fan Zhang, Anna Breger, Kang Ik Kevin Cho, Lipeng Ning, Carl-Fredrik Westin, Lauren J. O’Donnell, Ofer Pasternak
doi: https://doi.org/10.1101/2020.07.30.228809
Fan Zhang
cDepartment of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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  • For correspondence: fzhang@bwh.harvard.edu
Anna Breger
bFaculty of Mathematics, University of Vienna, Wien, Austria
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Kang Ik Kevin Cho
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Lipeng Ning
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Carl-Fredrik Westin
cDepartment of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Lauren J. O’Donnell
cDepartment of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Ofer Pasternak
cDepartment of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Abstract

Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in the dMRI data as compared with the anatomical MRI data. In this study, we present a deep learning method that learns tissue segmentation from high-quality imaging datasets from the Human Connectome Project (HCP), where registration of anatomical data to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with a different acquisition protocol, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from a clinical acquisition with lower resolution and fewer gradient directions.

Competing Interest Statement

The authors have declared no competing interest.

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Posted July 31, 2020.
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Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI
Fan Zhang, Anna Breger, Kang Ik Kevin Cho, Lipeng Ning, Carl-Fredrik Westin, Lauren J. O’Donnell, Ofer Pasternak
bioRxiv 2020.07.30.228809; doi: https://doi.org/10.1101/2020.07.30.228809
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Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI
Fan Zhang, Anna Breger, Kang Ik Kevin Cho, Lipeng Ning, Carl-Fredrik Westin, Lauren J. O’Donnell, Ofer Pasternak
bioRxiv 2020.07.30.228809; doi: https://doi.org/10.1101/2020.07.30.228809

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