用图卷积网络从分割图快速生成骨骼模型。
Fitting Skeletal Models via Graph-based Learning
- 基于图卷积网络，从密集分割掩码直接生成骨架表示
- 在合成数据和真实海马体数据上均取得良好效果，推理速度快
- 适合需要高效骨架提取的医学图像分析任务
骨架化是一种流行的形状分析技术，将物体内部结构建模而非仅边界。传统基于模板的骨架拟合过程耗时且需大量人工调参。近年来，基于机器学习的方法在从物体边界生成s-reps方面展现出潜力。本文提出一种新骨架化方法，利用图卷积网络从密集分割掩码生成骨架表示（s-reps）。该方法在合成数据和真实海马体分割数据上进行了评估，结果表现良好，且推理速度快。
原文摘要 · Abstract (English)
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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