Abstract
The study of tracts—bundles of nerve fibers that are organized together and have a similar function—is of major interest in neurology and related areas of science. Tractography is the medical imaging technique that provides the information to estimate these tracts, which is crucial for clinical applications and scientific research. This is a complex task due to the nature of the nerve fibers, also known as streamlines, and requires human interaction with prior knowledge. In this paper, we propose an automatic volumetric segmentation architecture based on the 3D U-Net architecture to segment each tract individually from streamlines data. We evaluate the impact of different data pre-processing techniques namely Rescaled Density Map (RDM), Gaussian Filter Mask (GFM), and Closing Opening Mask (COM) on the final segmentation results using the Tractoinferno dataset. In our experiments, the average DICE and IoU average performance was 62.2% and 72.2% respectively. Our results show that proper data pre-processing can significantly enhance segmentation performance. Moreover, we achieve similar levels of accuracy for all segmented tracts, despite shape disparity and an unequal number of occurrences in the tract dataset. Overall, this work contributes to the field of neuroimaging by providing a reliable approach for accurately segmenting individual tracts.
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Rocamora-García, P., Saval-Calvo, M., Villena-Martinez, V., Gallego, A.J. (2023). A Deep Approach for Volumetric Tractography Segmentation. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_46
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