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arXiv · 1907.07077

Anatomically-Informed Multiple Linear Assignment Problems for White Matter Bundle Segmentation

Giulia Bertò·Paolo Avesani·Franco Pestilli·Daniel Bullock·Bradley Caron·Emanuele Olivetti
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Abstract

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.

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Giulia Bertò, Paolo Avesani, Franco Pestilli, Daniel Bullock, Bradley Caron, Emanuele Olivetti. 2019-07-16. Anatomically-Informed Multiple Linear Assignment Problems for White Matter Bundle Segmentation. https://doi.org/10.1109/isbi.2019.8759174

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