骨点表示与几何深度学习
Ninad Khargonkar1, Beatriz Paniagua2, Jared Vicory2
1The University of Texas at Dallas, Department of Computer Science, Richardson, Texas.
Proceedings. IEEE International Symposium on Biomedical Imaging
|January 16, 2024
概括
本研究介绍了使用深度学习和新的几何术语创建3D形状骨的更快方法. 该技术准确地模拟了解剖结构,改进了传统耗时的手工工艺.
科学领域:
- 计算机视觉 计算机视觉
- 医疗成像医学成像
- 几何建模 几何建模
背景情况:
- 骨架化是一种用于对象建模的关键形状分析技术.
- 从解剖结构中创建骨模型的传统方法是手工和耗时的.
- 基于学习的方法为自动化从3D形状中提取骨提供了潜力.
研究的目的:
- 开发一种新的,有效的方法来计算物体的骨架结构.
- 为了提高骨架化的速度,同时保持与传统方法相比的准确性.
- 在实际的临床数据上对拟议的方法进行评估,用于解剖结构建模.
主要方法:
- 提出了用于骨架计算的新增几何术语.
- 利用基于学习的方法从3D形状中提取骨.
- 雇员代表 (简单的代表) 作为模型的弱监督.
主要成果:
- 提出的方法比传统的基于模板的方法产生骨结构的速度要快得多.
- 结果与传统的装配式s-reps.相似.
- 对临床数据的评估证明了精确的骨代表性预测.
结论:
- 新的几何术语提高了基于学习的骨架化效率和准确性.
- 该方法为解剖结构的手动骨架化提供了一个更快的替代方案.
- 这种方法对医学成像和形状分析的应用非常有希望.
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