Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation / Bertò, G.; Bullock, D.; Astolfi, P.; Hayashi, S.; Zigiotto, L.; Annicchiarico, L.; Corsini, F.; De Benedictis, A.; Sarubbo, S.; Pestilli, F.; Avesani, P.; Olivetti, E.. - In: NEUROIMAGE. - ISSN 1053-8119. - ELETTRONICO. - 224:(2020), p. 117402. [10.1016/j.neuroimage.2020.117402]

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation

Bertò G.;Astolfi P.;Zigiotto L.;Sarubbo S.;Olivetti E.
2020

Abstract

Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.
Bertò, G.; Bullock, D.; Astolfi, P.; Hayashi, S.; Zigiotto, L.; Annicchiarico, L.; Corsini, F.; De Benedictis, A.; Sarubbo, S.; Pestilli, F.; Avesani, P.; Olivetti, E.
Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation / Bertò, G.; Bullock, D.; Astolfi, P.; Hayashi, S.; Zigiotto, L.; Annicchiarico, L.; Corsini, F.; De Benedictis, A.; Sarubbo, S.; Pestilli, F.; Avesani, P.; Olivetti, E.. - In: NEUROIMAGE. - ISSN 1053-8119. - ELETTRONICO. - 224:(2020), p. 117402. [10.1016/j.neuroimage.2020.117402]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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/11572/290025
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? 4
  • Scopus 9
  • ???jsp.display-item.citation.isi??? ND
social impact