2024/10/28 by Roua Rouatbi, Rouatbi, Roua, Cardona, Juan-Esteban Suarez +3 · 1 citation
Computer Science · #Advanced Vision and Imaging #Computational Geometry (cs.CG) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Image and Object Detection Techniques #Medical Image Segmentation Techniques #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2410.21004
openalex publication_date 2024/10/28 · openalex created_date 2024/11/14 · openalex updated_date 2026/07/31
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.