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White Matter Tract Segmentation as Multiple Linear Assignment Problems. | LitMetric

White Matter Tract Segmentation as Multiple Linear Assignment Problems.

Front Neurosci

NeuroInformatics Laboratory, Bruno Kessler Foundation, Trento, Italy.

Published: February 2018

AI Article Synopsis

  • Diffusion magnetic resonance imaging (dMRI) enables the visualization of brain axon pathways as streamlines, collectively termed a tractogram, which can be organized into anatomical structures called tracts for applications like neurosurgery.
  • The study focuses on supervised tract segmentation, utilizing prior knowledge from anatomical atlases or example segmented tracts from other individuals to ensure anatomically meaningful results, as opposed to unsupervised methods that may not guarantee accuracy.
  • The novel approach presented formulates streamline correspondence as a linear assignment problem, enhancing upon traditional nearest neighbor strategies by enforcing a one-to-one correspondence, thereby accommodating local anatomical variations, while also introducing an efficient computation method and a ranking strategy for merging correspondences.

Article Abstract

Diffusion magnetic resonance imaging (dMRI) allows to reconstruct the main pathways of axons within the white matter of the brain as a set of polylines, called streamlines. The set of streamlines of the whole brain is called the tractogram. Organizing tractograms into anatomically meaningful structures, called tracts, is known as the tract segmentation problem, with important applications to neurosurgical planning and tractometry. Automatic tract segmentation techniques can be unsupervised or supervised. A common criticism of unsupervised methods, like clustering, is that there is no guarantee to obtain anatomically meaningful tracts. In this work, we focus on supervised tract segmentation, which is driven by prior knowledge from anatomical atlases or from examples, i.e., segmented tracts from different subjects. We present a supervised tract segmentation method that segments a given tract of interest in the tractogram of a new subject using multiple examples as prior information. Our proposed tract segmentation method is based on the idea of streamline correspondence i.e., on finding corresponding streamlines across different tractograms. In the literature, streamline correspondence has been addressed with the nearest neighbor (NN) strategy. Differently, here we formulate the problem of streamline correspondence as a linear assignment problem (LAP), which is a cornerstone of combinatorial optimization. With respect to the NN, the LAP introduces a constraint of one-to-one correspondence between streamlines, that forces the correspondences to follow the local anatomical differences between the example and the target tract, neglected by the NN. In the proposed solution, we combined the Jonker-Volgenant algorithm (LAPJV) for solving the LAP together with an efficient way of computing the nearest neighbors of a streamline, which massively reduces the total amount of computations needed to segment a tract. Moreover, we propose a ranking strategy to merge correspondences coming from different examples. We validate the proposed method on tractograms generated from the human connectome project (HCP) dataset and compare the segmentations with the NN method and the ROI-based method. The results show that LAP-based segmentation is vastly more accurate than ROI-based segmentation and substantially more accurate than the NN strategy. We provide a Free/OpenSource implementation of the proposed method.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5808221PMC
http://dx.doi.org/10.3389/fnins.2017.00754DOI Listing

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