Fitting Skeletal Models via Graph-based Learning
<|reference_start|>Fitting Skeletal Models via Graph-based Learning: 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.<|reference_end|>
@article{gaggion2024fitting,
    title={Fitting Skeletal Models via Graph-based Learning},
    author={Nicol'as Gaggion and Enzo Ferrante and Beatriz Paniagua and Jared
  Vicory},
    journal={2024 IEEE International Symposium on Biomedical Imaging (ISBI),
  pp. 1-4, 2024},
    year={2024},
    doi={10.1109/ISBI56570.2024.10635871},
    archivePrefix={arXiv},
    eprint={2409.05311},
    primaryClass={cs.CV}
}