REVIEW article
Front. Artif. Intell.
Sec. Pattern Recognition
Volume 8 - 2025 | doi: 10.3389/frai.2025.1671099
Shape modeling of longitudinal medical images: from diffeomorphic metric mapping to deep learning
Provisionally accepted
- 1Delft University of Technology, Delft, Netherlands
- 2Technische Universiteit Delft, Delft, Netherlands
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Living biological tissue is a complex system, constantly growing and changing in response to external and internal stimuli. These processes lead to remarkable and intricate changes in shape. Modeling and understanding both natural and pathological (or abnormal) changes in the shape of anatomical structures is highly relevant, with applications in diagnostic, prognostic, and therapeutic healthcare. Nevertheless, modeling the longitudinal shape change of biological tissue is a non-trivial task due to its inherent nonlinear nature. In this review, we highlight several existing methodologies and tools for modeling longitudinal shape change (i.e., spatiotemporal shape modeling). These methods range from diffeomorphic metric mapping to deep-learning based approaches (e.g., autoencoders, generative networks, recurrent neural networks, etc.). We discuss the synergistic combinations of existing technologies and potential directions for future research, underscoring key deficiencies in the current research landscape.
Keywords: deep learning, Shape Modelling, spatiotemporal, medical imaging, Diffeomorphisms, longitudinal data
Received: 22 Jul 2025; Accepted: 10 Oct 2025.
Copyright: © 2025 Tay, Tumer and Zadpoor. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Edwin Tay, e.w.s.tay@tudelft.nl
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.