A Continuous and Interpretable Morphometric for Robust Quantification of Dynamic Biological Shapes

MCML Authors

Juan-Esteban Suarez Cardona

Dr.

→ Group Gitta Kutyniok
 Mathematical Foundations of Artificial Intelligence

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.

inproceedings RSV+26

---

ISBI 2026

IEEE 23rd International Symposium on Biomedical Imaging. London, UK, Apr 08-11, 2026.

---

Authors

R. Rouatbi • J.-E. Suarez Cardona • A. Villaronga-Luque • J. V. Veenvliet • I. F. Sbalzarini

---

Links

DOI

---

In Collaboration

---

Research Area

 A2 | Mathematical Foundations