Background. Functional assessment of right ventricles (RV) using gated myocardial perfusion single-photon emission computed tomography (MPS) heavily relies on the precise extraction of right ventricular contours. In this paper, we present a new deep learning model integrating both the spatial and temporal features in SPECT images to perform the segmentation of RV epicardium and endocardium. Methods. By integrating the spatial features from each cardiac frame of gated MPS and the temporal features from the sequential cardiac frames of the gated MPS, we develop a Spatial-Temporal V-Net (S-T-V-Net) for automatic extraction of RV endocardial and epicardial contours. In the S-T-V-Net, a V-Net is employed to hierarchically extract spatial features, and convolutional long-term short-term memory (ConvLSTM) units are added to the skip-connection pathway to extract the temporal features. The input of the S-T-V-Net is an ECG-gated sequence of the SPECT images and the output is the probability map of the endocardial or epicardial masks. A Dice similarity coefficient (DSC) loss which penalizes the discrepancy between the model prediction and the ground truth is adopted to optimize the segmentation model. Results. Our segmentation model was trained and validated on a retrospective dataset with 34 subjects, and the cardiac cycle of each subject was divided into 8 gates. The proposed ST-V-Net achieved a DSC of 0.7924 and 0.8227 for the RV endocardium and epicardium, respectively. The mean absolute error, the mean squared error, and the Pearson correlation coefficient of the RV ejection fraction between the ground truth and the model prediction are 0.0907, 0.0130 and 0.8411. Conclusion. The results demonstrate that the proposed ST-V-Net is an effective model for RV segmentation. It has great promise for clinical use in RV functional assessment.

翻译：右心室( RV) 功能评估 右心室( RV), 使用 Gated 心肌梗塞透透度单发热解剖面分析( MPS), 严重依赖右心心室轮廓剖面的精确提取。 在本文中, 我们展示了一个新的深层次学习模型, 包括SPECT图像中的空间和时间特征, 以进行 RV 震中和内心的分割。 方法 。 通过整合了 Gated MPS 的每个心脏框架的空间特征以及 Gated MPS 的顺序心脏框架的时间特征, 我们开发了空间- 时间- 肝脏流流流流流流流流数据( S- T- V- Net), 用于自动提取 RVVE 心室和震中骨架的自动提取 。 在 SVE- Talalalal- messionalational dality 中, REral- disalation A- dreal- dreal dal dal dalationsalations, 和RElegildal dreal dreal dreal dal dal dal dal, 。