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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.21004 |
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| _version_ | 1866918177980547072 |
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| author | Rouatbi, Roua Cardona, Juan-Esteban Suarez Villaronga-Luque, Alba Veenvliet, Jesse V. Sbalzarini, Ivo F. |
| author_facet | Rouatbi, Roua Cardona, Juan-Esteban Suarez Villaronga-Luque, Alba Veenvliet, Jesse V. Sbalzarini, Ivo F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21004 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Continuous and Interpretable Morphometric for Robust Quantification of Dynamic Biological Shapes Rouatbi, Roua Cardona, Juan-Esteban Suarez Villaronga-Luque, Alba Veenvliet, Jesse V. Sbalzarini, Ivo F. Computer Vision and Pattern Recognition Computational Geometry Quantitative Methods 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. |
| title | A Continuous and Interpretable Morphometric for Robust Quantification of Dynamic Biological Shapes |
| topic | Computer Vision and Pattern Recognition Computational Geometry Quantitative Methods |
| url | https://arxiv.org/abs/2410.21004 |