White Matter Tract Segmentation as Multiple Linear Assignment Problems

Frontiers in Neuroscience
Nusrat SharminPaolo Avesani

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...Continue Reading

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Citations

Jun 24, 2021·Medical Image Analysis·Jon Haitz LegarretaPierre-Marc Jodoin

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Methods Mentioned

BETA
imaging technique
dissection

Software Mentioned

scipy
FSL
Python
Numpy
LAPJV
spatial
LAP
MULTI
FLIRT
Cython

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