The aim of this study is to develop an automated deep-learning-based whole heart segmentation of ECG-gated computed tomography data. After 21 exclusions, CT acquired before transcatheter aortic valve implantation in 71 patients were reviewed and randomly split in a training (n = 55 patients), validation (n = 8 patients), and a test set (n = 8 patients). A fully automatic deep-learning method combining two convolutional neural networks performed segmentation of 10 cardiovascular structures, which was compared with the manually segmented reference by the Dice index. Correlations and agreement between myocardial volumes and mass were assessed. The algorithm demonstrated high accuracy (Dice score = 0.920; interquartile range: 0.906–0.925) and a low computing time (13.4 s, range 11.9–14.9). Correlations and agreement of volumes and mass were satisfactory for most structures. Six of ten structures were well segmented. Deep-learning-based method allowed automated WHS from ECG-gated CT data with a high accuracy. Challenges remain to improve right-sided structures segmentation and achieve daily clinical application.

Graphical abstract

中文翻译：

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使用深度学习方法验证计算机断层扫描成像的全心分割

本研究的目的是开发一种基于深度学习的自动心电门控计算机断层扫描数据的全心脏分割。在 21 次排除后，对 71 名患者在经导管主动脉瓣植入术前获得的 CT 进行了审查，并在训练（n  = 55 名患者）、验证（n  = 8 名患者）和测试集（n = 8 名患者）。结合两个卷积神经网络的全自动深度学习方法对 10 个心血管结构进行了分割，并通过 Dice 指数与手动分割的参考进行了比较。评估了心肌体积和质量之间的相关性和一致性。该算法展示了高精度（骰子得分 = 0.920；四分位距：0.906-0.925）和低计算时间（13.4 秒，范围 11.9-14.9）。大多数结构的体积和质量的相关性和一致性都令人满意。十个结构中有六个被很好地分割。基于深度学习的方法允许以高精度从 ECG 门控 CT 数据中自动进行 WHS。改善右侧结构分割和实现日常临床应用仍然存在挑战。

图形概要