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.
- Downloaded 129 times
- Download rankings, all-time:
- Site-wide: 90,038 out of 101,077
- In neuroscience: 16,010 out of 18,003
- Year to date:
- Site-wide: 49,566 out of 101,077
- Since beginning of last month:
- Site-wide: 18,621 out of 101,077
Downloads over time
Distribution of downloads per paper, site-wide
- 20 Oct 2020: Support for sorting preprints using Twitter activity has been removed, at least temporarily, until a new source of social media activity data becomes available.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!