There is an urgent unmet need to develop a fully-automated image-based left ventricle mitral valve analysis tool to support surgical decision making for ischemic mitral regurgitation patients. This requires an automated tool for segmentation and modeling of the left ventricle and mitral valve from immediate pre-operative 3D transesophageal echocardiography. Previous works have presented methods for semi-automatically segmenting and modeling the mitral valve, but do not include the left ventricle and do not avoid self-intersection of the mitral valve leaflets during shape modeling. In this study, we develop and validate a fully automated algorithm for segmentation and shape modeling of the left ventricular mitral valve complex from pre-operative 3D transesophageal echocardiography. We performed a 3-fold nested cross validation study on two datasets from separate institutions to evaluate automated segmentations generated by nnU-net with the expert manual segmentation which yielded average overall Dice scores of $0.82\pm 0.03$ (set A), $0.87\pm 0.08$ (set B) respectively. A deformable medial template was subsequently fitted to the segmentation to generate shape models. Comparison of shape models to the manual and automatically generated segmentations resulted in an average Dice score of 0.93-0.94 and 0.75-0.81 for the left ventricle and mitral valve, respectively. This is a substantial step towards automatically analyzing the left ventricle mitral valve complex in the operating room.

迫切需要开发一种基于图像的全自动左心室二尖瓣分析工具，以支持缺血性二尖瓣关闭不全患者的手术决策。这需要一个自动化工具，用于根据术前 3D 经食道超声心动图对左心室和二尖瓣进行分割和建模。以前的工作已经提出了半自动分割和建模二尖瓣的方法，但不包括左心室，并且在形状建模期间不避免二尖瓣小叶的自相交。在这项研究中，我们开发并验证了一种全自动算法，用于根据术前 3D 经食道超声心动图对左心室二尖瓣复合体进行分割和形状建模。$0.82\pm 0.03$(A 组),$0.87\pm 0.08$（B组）分别。随后将可变形的内侧模板拟合到分割中以生成形状模型。将形状模型与手动和自动生成的分割进行比较，导致左心室和二尖瓣的平均 Dice 得分分别为 0.93-0.94 和 0.75-0.81。这是朝着在手术室自动分析左心室二尖瓣复合体迈出的重要一步。