Tractograms are virtual representations of the white matter fibers of the brain. They are of primary interest for tasks like presurgical planning, and investigation of neuroplasticity or brain disorders. Each tractogram is composed of millions of fibers encoded as 3D polylines. Unfortunately, a large portion of those fibers are not anatomically plausible and can be considered artifacts of the tracking algorithms. Common methods for tractogram filtering are based on signal reconstruction, a principled approach, but unable to consider the knowledge of brain anatomy. In this work, we address the problem of tractogram filtering as a supervised learning problem by exploiting the ground truth annotations obtained with a recent heuristic method, which labels fibers as either anatomically plausible or non-plausible according to well-established anatomical properties. The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties. Our contribution is an extension of the Dynamic Edge Convolution model that exploits the sequential relations of points in a fiber and discriminates with high accuracy plausible/non-plausible fibers.

Tractogram Filtering of Anatomically Non-plausible Fibers with Geometric Deep Learning / Astolfi, P.; Verhagen, R.; Petit, L.; Olivetti, E.; Masci, J.; Boscaini, D.; Avesani, P.. - 12267:(2020), pp. 291-301. ((Intervento presentato al convegno 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 tenutosi a per nel 2020 [10.1007/978-3-030-59728-3_29].

Tractogram Filtering of Anatomically Non-plausible Fibers with Geometric Deep Learning

Astolfi P.;Olivetti E.;
2020

Abstract

Tractograms are virtual representations of the white matter fibers of the brain. They are of primary interest for tasks like presurgical planning, and investigation of neuroplasticity or brain disorders. Each tractogram is composed of millions of fibers encoded as 3D polylines. Unfortunately, a large portion of those fibers are not anatomically plausible and can be considered artifacts of the tracking algorithms. Common methods for tractogram filtering are based on signal reconstruction, a principled approach, but unable to consider the knowledge of brain anatomy. In this work, we address the problem of tractogram filtering as a supervised learning problem by exploiting the ground truth annotations obtained with a recent heuristic method, which labels fibers as either anatomically plausible or non-plausible according to well-established anatomical properties. The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties. Our contribution is an extension of the Dynamic Edge Convolution model that exploits the sequential relations of points in a fiber and discriminates with high accuracy plausible/non-plausible fibers.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Switzerland
Springer Science and Business Media Deutschland GmbH
978-3-030-59727-6
978-3-030-59728-3
Astolfi, P.; Verhagen, R.; Petit, L.; Olivetti, E.; Masci, J.; Boscaini, D.; Avesani, P.
Tractogram Filtering of Anatomically Non-plausible Fibers with Geometric Deep Learning / Astolfi, P.; Verhagen, R.; Petit, L.; Olivetti, E.; Masci, J.; Boscaini, D.; Avesani, P.. - 12267:(2020), pp. 291-301. ((Intervento presentato al convegno 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 tenutosi a per nel 2020 [10.1007/978-3-030-59728-3_29].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/296083
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