Fast Multi-label Parameterization of the Left Atrium by Learned Template Morphing
Castañeda E, Raghunath A, Meister F, Mihalef V, Ashikaga H, Maier A, Passerini T, Lluch È (2026)
Publication Type: Conference contribution
Publication year: 2026
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 16459 LNCS
Pages Range: 261-271
Conference Proceedings Title: Lecture Notes in Computer Science
Event location: Daejeon
ISBN: 9783032177339
DOI: 10.1007/978-3-032-17734-6_25
Abstract
The shape of the left atrium is a possibly critical biomarker for predicting the risk of atrial fibrillation. Traditional statistical shape analysis necessitates point correspondences across anatomical surface meshes, typically achieved by morphing a template mesh to a dataset of surface meshes using deformable registration techniques. While traditional optimization-based deformable registration methods yield excellent surface matching results, they are computationally intensive, hindering large-scale shape analysis. Furthermore, traditional deformable registration methods do not utilize multi-label surface meshes to guide the deformation process. In this study, we propose a novel learning-based multi-label deformable registration method for LA surface parameterization. Our method demonstrates surface matching results comparable to traditional methods, while maintaining the anatomical accuracy of the ostia of the pulmonary veins and the left atrial appendage, at considerably faster execution times.
Authors with CRIS profile
Adarsh Raghunath
Lehrstuhl für Informatik 5 (Mustererkennung)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Involved external institutions
Germany (DE)
United States (USA) (US)How to cite
APA:
Castañeda, E., Raghunath, A., Meister, F., Mihalef, V., Ashikaga, H., Maier, A.,... Lluch, È. (2026). Fast Multi-label Parameterization of the Left Atrium by Learned Template Morphing. In Oscar Camara, Esther Puyol Antón, Charlène Mauge, Alistair Young, Maxime Sermesant, Marta Varela, Yingliang Ma, Rasmus Paulsen, Chengyan Wang, Qian Tao (Eds.), Lecture Notes in Computer Science (pp. 261-271). Daejeon, KR: Springer Science and Business Media Deutschland GmbH.
MLA:
Castañeda, Eduardo, et al. "Fast Multi-label Parameterization of the Left Atrium by Learned Template Morphing." Proceedings of the 16th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2025, Held in Conjunction with MICCAI 2025, Daejeon Ed. Oscar Camara, Esther Puyol Antón, Charlène Mauge, Alistair Young, Maxime Sermesant, Marta Varela, Yingliang Ma, Rasmus Paulsen, Chengyan Wang, Qian Tao, Springer Science and Business Media Deutschland GmbH, 2026. 261-271.
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