A Continuous and Interpretable Morphometric for Robust Quantification of Dynamic Biological Shapes
This provides a continuous and interpretable morphometric for biomedical imaging researchers, offering a novel method for shape quantification with applications in developmental biology.
The paper tackles the problem of quantifying dynamic biological shapes in biomedical imaging by introducing the Push-Forward Signed Distance Morphometric (PF-SDM), which encodes geometric and topological properties for robust shape comparison and machine learning. It outperforms a CNN baseline in predicting body-axis formation in mouse gastruloids, achieving better accuracy and speed.
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.