Segmenting white matter bundles from human tractograms is a task of interest for several applications. Current methods for bundle segmentation consider either only prior knowledge about the relative anatomical position of a bundle, or only its geometrical properties. Our aim is to improve the results of segmentation by proposing a method that takes into account information about both the underlying anatomy and the geometry of bundles at the same time. To achieve this goal, we extend a state-of-the-art example-based method based on the Linear Assignment Problem (LAP) by including prior anatomical information within the optimization process. The proposed method shows a significant improvement with respect to the original method, in particular on small bundles.
@article{arxiv.1907.07077,
title = {Anatomically-Informed Multiple Linear Assignment Problems for White Matter Bundle Segmentation},
author = {Giulia Bertò and Paolo Avesani and Franco Pestilli and Daniel Bullock and Bradley Caron and Emanuele Olivetti},
journal= {arXiv preprint arXiv:1907.07077},
year = {2019}
}