Abstract
Accurate segmentation of cerebral structures remains, after two decades of research, a complex task. In particular, obtaining satisfactory results in terms of topology, in addition to quantitative and geometrically correct properties is still an ongoing issue. In this paper, we investigate how recent advances in multilabel topology and homotopy-type preserving transformations can be involved in the development of multiscale topological modelling of brain structures, and topology-based post-processing of segmentation maps of brain MR images. In this context, a preliminary study and a proof-of-concept are presented.
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Acknowledgements
The research leading to these results has been supported by the ANR MAIA project (http://recherche.imt-atlantique.fr/maia), grant ANR-15-CE23-0009 of the French National Research Agency; INSERM and Institut Mines Télécom Atlantique (Chaire “Imagerie médicale en thérapie interventionnelle”); the Fondation pour la Recherche Médicale (grant DIC20161236453); and the American Memorial Hospital Foundation.
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Tor-Díez, C. et al. (2019). Multilabel, Multiscale Topological Transformation for Cerebral MRI Segmentation Post-processing. In: Burgeth, B., Kleefeld, A., Naegel, B., Passat, N., Perret, B. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2019. Lecture Notes in Computer Science(), vol 11564. Springer, Cham. https://doi.org/10.1007/978-3-030-20867-7_36
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