3D Transesophageal Echocardiography is an excellent tool for evaluating the mitral valve and is also well suited for guiding cardiac interventions. We introduce a fully automatic method for mitral annulus segmentation in 3D Transesophageal Echocardiography, which requires no manual input. One hundred eleven multi-frame 3D transesophageal echocardiography recordings were split into training, validation, and test sets. Each 3D recording was decomposed into a set of 2D planes, exploiting the symmetry around the centerline of the left ventricle. A deep 2D convolutional neural network was trained to predict the mitral annulus coordinates, and the predictions from neighboring planes were regularized by enforcing continuity around the annulus. Applying the final model and post-processing to the test set data gave a mean error of 2.0 mm — with a standard deviation of 1.9 mm. Fully automatic segmentation of the mitral annulus can alleviate the need for manual interaction in the quantification of an array of mitral annular parameters and has the potential to eliminate inter-observer variability.

中文翻译：

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在3D经食管超声心动图中使用深度学习进行二尖瓣环分割

3D经食道超声心动图是评估二尖瓣的绝佳工具，也非常适合指导心脏干预。我们在3D经食管超声心动图中引入了一种用于二尖瓣环分割的全自动方法，该方法不需要人工输入。一百一十一幅多帧3D经食道超声心动图记录被分为训练，验证和测试集。利用围绕左心室中心线的对称性，将每个3D记录分解为一组2D平面。训练了一个深层的2D卷积神经网络来预测二尖瓣环的坐标，并且通过强制环周围的连续性来规范来自相邻平面的预测。将最终模型应用于测试集数据并进行后处理，得出的平均误差为2。0毫米-标准偏差为1.9毫米。二尖瓣环的全自动分割可以减轻对二尖瓣环参数阵列进行量化时手动交互的需要，并且有可能消除观察者之间的差异。