Fitting Skeletal Models via Graph-based Learning

• URL: http://arxiv.org/abs/2409.05311v1
• Date: Mon, 9 Sep 2024 03:50:41 GMT
• Title: Fitting Skeletal Models via Graph-based Learning
• Authors: Nicolás Gaggion, Enzo Ferrante, Beatriz Paniagua, Jared Vicory,
• Abstract summary: We propose a new skeletonization method which leverages graph convolutional networks to produce skeletal representations (s-reps) from dense segmentation masks. The method is evaluated on both synthetic data and real hippocampus segmentations, achieving promising results and fast inference.
• Score: 3.059114987144684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Skeletonization is a popular shape analysis technique that models an object's interior as opposed to just its boundary. Fitting template-based skeletal models is a time-consuming process requiring much manual parameter tuning. Recently, machine learning-based methods have shown promise for generating s-reps from object boundaries. In this work, we propose a new skeletonization method which leverages graph convolutional networks to produce skeletal representations (s-reps) from dense segmentation masks. The method is evaluated on both synthetic data and real hippocampus segmentations, achieving promising results and fast inference.

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