Research Ideas and Outcomes : Project Report
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Corresponding author: Kesshi M Jordan (kesshi.jordan@ucsf.edu)
Received: 23 Feb 2017 | Published: 24 Feb 2017
© 2017 Kesshi Jordan, Anisha Keshavan, Maria Luisa Mandelli, Roland Henry
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Jordan K, Keshavan A, Mandelli M, Henry R (2017) Cluster-viz: A Tractography QC Tool. Research Ideas and Outcomes 3: e12394. https://doi.org/10.3897/rio.3.e12394
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Cluster-viz is a web application that provides a platform for cluster-based interactive quality-control of tractography algorithm outputs. This tool facilitates the creation of white matter fascicle models by employing a cluster-based approach to allow the user to select streamline bundles for inclusion/exclusion in the final fascicle model. This project was started at the 2016 Neurohackweek and BrainHack events and is still under development. We welcome contributions to the Cluster-viz github repository (https://github.com/kesshijordan/Cluster-viz).
Fiber Tracking, Streamline Clustering, Web Application, Fascicle Model, Quality Control
When tractography algorithms are used to create an anatomically constrained model of a fascicle, the output of the processing can contain many streamlines that are not part of the bundle-of-interest. Using methods that leverage High Angular Resolution Diffusion Imaging (HARDI) datasets by employing models like Constrained Spherical Deconvolution (
This viewer enables the user to select streamlines on a cluster-level (Fig.
The connectivity of an ROI placed on the coronal plane over the external/extreme capsules at the level of the anterior commissure is shown (tractography method:
The user selected two sub-bundles that contain streamlines representing a tractography model of the Uncinate Fasciculus.
This Cluster-Based Streamline Tool was implemented as a web-based viewer with a python backend using CherryPy (Fig.
This method is advantageous to the traditional ROI-based approach because binary decisions made on discrete clusters is less variable than manually placing ROIs in continuous space. In theory, this should facilitate reproducibility of human operators, as well as create a more tractable training set for machine learning applications. Ideally, the Cluster-viz tool would learn from the user as they interact with the viewer and provide suggestions for bundle classification that the user could approve. Over time, the learning element could greatly increase the efficiency of the user and, perhaps, eventually replace the human.
This work was completed during Neurohackweek 2016 in Seattle, WA and the BrainHack 2016 in Los Angeles, CA. We would like to thank Dr. Ariel Rokem and Dr. Jason Yeatman for their help during Neurohackweek and Dr. Jeremy Maitin-Shepard for his help during BrainHack LA. We would also like to thank all of the Neurohackweek and BrainHack organizers and mentors.