Deep feature regression (DFR) for 3D vessel segmentation.

The structural information of coronary arteries has important clinical value for quantitative diagnosis and treatment of coronary artery disease. In this study, a deep feature regression (DFR) method based on a convolutional regression network (CRN) and a stable point clustering mechanism for 3D vessel segmentation is proposed. First, the vessel model is constructed by a vessel section generator and a series of deviation parameter estimators. The generator provides 2D images for the training and prediction processes, while the estimators calculate pose parameters of an input vessel section. Second, estimators are trained by a series of CRNs, in which deep vessel features are automatically learned from 600 000 sample images. Third, we propose a stable point clustering mechanism that evaluates the reliability of the CRN estimation through iterative regression of vessel parameters. This mechanism eliminates the outliers, thereby increasing tracking robustness. Finally, we present a vessel segmentation algorithm using trained deviation parameter estimators. And, the termination criteria are designed based on both the number of stable points and an intensity constraint. The proposed method is evaluated on a public coronary artery data set. The average overlapping ratio and error are 97.5% and 0.27 mm, respectively. A quantitative test on a public cerebral artery data set demonstrates that the proposed DFR method tracks the vessel centerline with high accuracy, for which the average error is less than 0.33 mm.