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.

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

Berto, Giulia;Avesani, Paolo;Olivetti, Emanuele
2019

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.
978-1-5386-3641-1
File in questo prodotto:
File Dimensione Formato  
anatomically_informed_isbi2019.pdf

solo utenti autorizzati

Descrizione: submitted manuscript
Tipologia: Documento in Pre-print
Licenza: PUBBLICO - Pubblico con Copyright
Dimensione 1 MB
Formato Adobe PDF
1 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/319684
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact