3D transesophageal echocardiography (3DTEE), is the recommended method for diagnosing mitral regurgitation (MR). 3DTEE provides a high-quality 3D image of the mitral valve (MV), allowing for precise segmentation and measurement of the regurgitant valve anatomy. However, manual TEE segmentations are time-consuming and prone to intra-operator variability, affecting the reliability of the measurements. To address this, we developed a fully automated pipeline using a 3D convolutional neural network (CNN) to segment MV substructures (annulus, anterior leaflet, and posterior leaflet) and quantify MV anatomy. The 3D CNN, based on a multi-decoder residual U-Net architecture, was trained and tested on a dataset comprising 100 3DTEE images with corresponding segmentations. Within the pipeline, a custom algorithm refines the CNN-based segmentations and extracts MV models, from which anatomical landmarks and features are quantified. The accuracy of the proposed method was assessed using Dice score and mean surface distance (MSD) against ground truth segmentations, and the extracted anatomical parameters were compared against a semiautomated commercial software TomTec Image Arena. The trained 3D CNN achieved an average Dice score of 0.79 and MSD of 0.47 mm for the combined segmentation of the annulus, anterior and posterior leaflet. The proposed CNN architecture outperformed a baseline residual U-Net architecture in MV substructure segmentation, and the refinement of the predicted annulus segmentation improved MSD by 8.36%. The annular and leaflet linear measurements differed by less than 7.94 mm and 3.67 mm, respectively, compared to the 3D measurements obtained with TomTec Image Arena. The proposed pipeline was faster than the commercial software, with a modeling time of 12.54 s and a quantification time of 54.42 s.

翻译：三维经食管超声心动图（3DTEE）是诊断二尖瓣反流（MR）的推荐方法。3DTEE可提供高质量的二尖瓣（MV）三维图像，从而实现对反流瓣膜解剖结构的精确分割与测量。然而，手动进行TEE分割耗时且易受操作者个体差异影响，降低了测量结果的可靠性。为此，我们开发了一种全自动流程，利用三维卷积神经网络（CNN）分割二尖瓣亚结构（瓣环、前叶和后叶）并量化二尖瓣解剖特征。该三维CNN基于多解码器残差U-Net架构，在一个包含100幅3DTEE图像及对应分割标注的数据集上进行了训练与测试。在该流程中，一个定制算法对基于CNN的分割结果进行优化并提取二尖瓣模型，进而量化解剖标志点与特征。我们采用Dice分数和平均表面距离（MSD）与金标准分割进行对比，以评估所提方法的准确性，并将提取的解剖参数与半自动化商业软件TomTec Image Arena的测量结果进行比较。训练完成的三维CNN在瓣环、前叶和后叶的联合分割任务中，平均Dice分数达到0.79，平均MSD为0.47毫米。所提出的CNN架构在二尖瓣亚结构分割任务中表现优于基准残差U-Net架构，且对预测瓣环分割结果的优化使MSD提升了8.36%。与TomTec Image Arena获得的三维测量结果相比，本方法测得的瓣环及瓣叶线性测量值差异分别小于7.94毫米和3.67毫米。所提流程的处理速度优于商业软件，其建模时间为12.54秒，量化时间为54.42秒。