DeepNuParc: A Novel Deep Clustering Framework for Fine-scale Parcellation of Brain Nuclei Using Diffusion MRI Tractography by Haolin He & Ce Zhu & Le Zhang & Yipeng Liu & Xiao Xu & Yuqian Chen & Leo Zekelman & Jarrett Rushmore & Yogesh Rathi & Nikos Makris & Lauren J. O'Donnell & Fan Zhang ISBN 250307263V2 instant download
In this work, we propose DeepNuParc, a novel deep clustering pipeline to perform
parcellation of the brain nuclei with dMRI tractography. In our proposed framework,
we first compute a novel voxel-wise connectivity feature representation, with a feature
refinement process using the newly proposed streamline cluster dilation and smoothing.
Next, we design an adaptive k-means-friendly autoencoder framework that can
compress the feature representation and jointly train with the downstream clustering
algorithm. Finally, we achieve fine-scale parcellation of the brain nucleus structure by
clustering voxels into different groups.
A crucial step in our DeepNuParc method involves the explicit reconstruction of
dMRI tractography streamlines to create the feature representation, as opposed to relying
on connectivity probabilities derived from probabilistic tractography [25, 26]. The
explicit reconstruction of tractography streamlines allows us to form streamlines that
pass through a nucleus into streamline clusters, thereby enriching the data representation.
Furthermore, by constructing each voxel’s feature vector based on its traversal by
streamline clusters, we transform a complex high-dimensional brain parcellation problem
into a simpler one-dimensional vector clustering problem. This approach enhances
both the simplicity and robustness of the algorithm.
Our algorithm has good flexibility and compatibility to be modified and customized.
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