CNRS, Inria, LORIA, Université de Lorraine, Nancy, France.
Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA.
Int J Comput Assist Radiol Surg. 2021 May;16(5):709-720. doi: 10.1007/s11548-021-02368-3. Epub 2021 May 12.
Mitral valve computational models are widely studied in the literature. They can be used for preoperative planning or anatomical understanding. Manual extraction of the valve geometry on medical images is tedious and requires special training, while automatic segmentation is still an open problem.
We propose here a fully automatic pipeline to extract the valve chordae architecture compatible with a computational model. First, an initial segmentation is obtained by sub-mesh topology analysis and RANSAC-like model-fitting procedure. Then, the chordal structure is optimized with respect to objective functions based on mechanical, anatomical, and image-based considerations.
The approach has been validated on 5 micro-CT scans with a graph-based metric and has shown an [Formula: see text] accuracy rate. The method has also been tested within a structural simulation of the mitral valve closed state.
Our results show that the chordae architecture resulting from our algorithm can give results similar to experienced users while providing an equivalent biomechanical simulation.
二尖瓣计算模型在文献中被广泛研究。它们可用于术前规划或解剖理解。在医学图像上手动提取阀几何形状既繁琐又需要特殊培训,而自动分割仍然是一个未解决的问题。
我们在这里提出了一种全自动流水线,用于提取与计算模型兼容的阀索结构。首先,通过子网格拓扑分析和类似于 RANSAC 的模型拟合过程获得初始分割。然后,根据机械、解剖和基于图像的考虑因素,针对目标函数对索状结构进行优化。
该方法已通过基于图的度量标准在 5 个微 CT 扫描上进行了验证,准确率达到[Formula: see text]。该方法还在二尖瓣关闭状态的结构模拟中进行了测试。
我们的结果表明,我们的算法产生的索状结构可以提供与经验丰富的用户相似的结果,同时提供等效的生物力学模拟。