| First Authors |
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| Authors |
Roua Rouatbi, Juan-Esteban Suarez Cardona, Alba Villaronga Luque, Jesse V Veenvliet, Ivo F. Sbalzarini
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| Corresponding Authors |
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| Last Authors |
Ivo F. Sbalzarini
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| Conference Proceedings Volume Title |
Proc. IEEE Intl. Symposium Biomedical Imaging (ISBI) |
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| Conference Name |
IEEE Intl. Symposium Biomedical Imaging (ISBI) |
| Conference Location |
London, UK |
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| Chapter Number |
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| Publisher |
IEEE |
| Conference Proceedings Editors |
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| ISBN |
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| First Page |
1 |
| Last Page |
5 |
| Open Access |
false |
| Print Publication Date |
2026-01-01 |
| Online Publication Date |
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| 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. |
| PDF |
Rouatbi_2026_9240.pdf
(2 MB)
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| Cover Image |
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| Affiliated With |
Sbalzarini
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| Selected By |
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| Acknowledged Services |
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| Publication Status |
Published |
| Edoc Link |
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| Sfx Link |
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| DOI |
10.1109/ISBI61048.2026.11515685 |
| PubMed ID |
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| WebOfScience Link |
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| Alternative Full Text URL |
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| Display Publisher Download Only |
false |
| Visible On MPI-CBG Website |
true |
| PDF Downloadable |
true |
| Created By |
sbalzari |
| Added Date |
2026-06-10 |
| Last Edited By |
sbalzari |
| Last Edited Date |
2026-06-10 08:57:59.543 |
| Library ID |
9240 |
| Document ID |
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| Entry Complete |
false |
| eDoc Compliant |
false |
| Include in Edoc Report |
false |
| In Pure |
false |
| Ready for eDoc Export |
false |
| Author Affiliations Complete |
false |
| Project Name |
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| Project URL |
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| Grant ID |
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| Funding Programme |
ScaDS.AI |
| Funding Organisation |
BMFTR |