2023/03/03 by Ninad Khargonkar, Khargonkar, Ninad, Beatriz Paniagua +3
Arts and Humanities · Engineering · Mathematics · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Forensic Anthropology and Bioarchaeology Studies #Machine Learning (cs.LG) #Medical Imaging and Analysis #Morphological variations and asymmetry
paper · pdf · doi:10.48550/arxiv.2303.02123
openalex publication_date 2023/03/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Skeletonization has been a popular shape analysis technique that models both the interior and exterior of an object. Existing template-based calculations of skeletal models from anatomical structures are a time-consuming manual process. Recently, learning-based methods have been used to extract skeletons from 3D shapes. In this work, we propose novel additional geometric terms for calculating skeletal structures of objects. The results are similar to traditional fitted s-reps but but are produced much more quickly. Evaluation on real clinical data shows that the learned model predicts accurate skeletal representations and shows the impact of proposed geometric losses along with using s-reps as weak supervision.