Three-dimensional transesophageal echocardiography (3DTEE) is the recommended imaging technique for the assessment of mitral valve (MV) morphology and lesions in case of mitral regurgitation (MR) requiring surgical or transcatheter repair. Such assessment is key to thorough intervention planning and to intraprocedural guidance. However, it requires segmentation from 3DTEE images, which is timeconsuming, operator-dependent, and often merely qualitative. In the present work, a novel workflow to quantify the patient-specific MV geometry from 3DTEE is proposed. The developed approach relies on a 3D multi-decoder residual convolutional neural network (CNN) with a U-Net architecture for multi-class segmentation of MV annulus and leaflets. The CNN was trained and tested on a dataset comprising 55 3DTEE examinations of MR-affected patients. After training, the CNN is embedded into a fully automatic, and hence fully repeatable, pipeline that refines the predicted segmentation, detects MV anatomical landmarks and quantifies MV morphology. The trained 3D CNN achieves an average Dice score of $0.82 \pm 0.06$, mean surface distance of $0.43 \pm 0.14$ mm and 95% Hausdorff Distance (HD) of $3.57 \pm 1.56$ mm before segmentation refinement, outperforming a state-of-the-art baseline residual U-Net architecture, and provides an unprecedented multi-class segmentation of the annulus, anterior and posterior leaflet. The automatic 3D linear morphological measurements of the annulus and leaflets, specifically diameters and lengths, exhibit differences of less than 1.45 mm when compared to ground truth values. These measurements also demonstrate strong overall agreement with analyses conducted by semi-automated commercial software. The whole process requires minimal user interaction and requires approximately 15 seconds

翻译：三维经食管超声心动图（3DTEE）是评估需要手术或经导管修复的二尖瓣反流（MR）患者二尖瓣（MV）形态和病变的推荐成像技术。此类评估对于全面的介入规划和术中引导至关重要。然而，它需要对3DTEE图像进行分割，这一过程耗时、依赖于操作者，且通常仅为定性分析。本研究提出了一种从3DTEE图像量化患者特异性二尖瓣几何形态的新工作流程。所开发的方法依赖于一个采用U-Net架构的三维多解码器残差卷积神经网络（CNN），用于二尖瓣瓣环和瓣叶的多类别分割。该CNN在包含55例MR患者的3DTEE检查数据集中进行了训练和测试。训练完成后，该CNN被嵌入到一个全自动（因而完全可重复）的流程中，该流程对预测的分割结果进行细化、检测二尖瓣解剖标志点并量化二尖瓣形态。训练好的3D CNN在分割细化前取得了平均Dice分数为$0.82 \pm 0.06$、平均表面距离为$0.43 \pm 0.14$ mm、95%豪斯多夫距离（HD）为$3.57 \pm 1.56$ mm的成绩，优于最先进的基线残差U-Net架构，并提供了前所未有的瓣环、前叶和后叶的多类别分割。对瓣环和瓣叶的自动三维线性形态学测量（特别是直径和长度）与真实值相比差异小于1.45 mm。这些测量结果也与半自动商业软件进行的分析表现出很强的总体一致性。整个过程仅需极少的用户交互，耗时约15秒。