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arXiv · 2410.21004

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

Roua Rouatbi·Juan-Esteban Suarez Cardona·Alba Villaronga-Luque·Jesse V. Veenvliet·Ivo F. Sbalzarini
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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.

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Roua Rouatbi, Juan-Esteban Suarez Cardona, Alba Villaronga-Luque, Jesse V. Veenvliet, Ivo F. Sbalzarini. 2024-10-28. A Continuous and Interpretable Morphometric for Robust Quantification of Dynamic Biological Shapes. https://arxiv.org/abs/2410.21004

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