| First Authors | Roua Rouatbi |
|---|---|
| Authors | Roua Rouatbi, Juan-Esteban Suarez Cardona, Alba Villaronga-Luque, Jesse V Veenvliet, Ivo F. Sbalzarini |
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| Last Authors | Ivo F. Sbalzarini |
| Journal Name | ArXiv (ArXiv) |
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| Article Number | ArXiv:2410.21004 |
| Open Access | true |
| Print Publication Date | |
| Online Publication Date | 2025-10-28 |
| Abstract | We introduce the Push-Forward Signed Distance Morphometric (PF-SDM) for shape quantification in biomedical imaging. The PF-SDM compactly encodes geometric and topological properties of closed shapes, including their skeleton and symmetries. This provides robust and interpretable features for shape comparison and machine learning. The PF-SDM is mathematically smooth, providing access to gradients and differential-geometric quantities. It also extends to temporal dynamics and allows fusing spatial intensity distributions, such as genetic markers, with shape dynamics. We present the PF-SDM theory, benchmark it on synthetic data, and apply it to predicting body-axis formation in mouse gastruloids, outperforming a CNN baseline in both accuracy and speed. |
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| Affiliated With | Veenvliet |
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| Publication Status | Published |
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| Alternative Full Text URL | https://doi.org/10.48550/arXiv.2410.21004 |
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| Created By | thuem |
| Added Date | 2026-01-09 |
| Last Edited By | thuem |
| Last Edited Date | 2026-01-09 10:31:43.721 |
| Library ID | 9118 |
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