Training Sequential CAG Segmentation Models Without Labeled CAG Video Data

Although sequential Coronary Angiograms (CAG) are often used in cardiac catheterizations, most vessel segmentation techniques are developed just for single images, owing to the expense of collecting and labeling huge amounts of sequential X-Ray images. The CAG segmentation using single images could cause the overlook of critical temporal information among images. Existing video segmentation methods, while they achieve high accuracy for daily life videos, cannot be used directly for sequential CAG, because they are not trained to capture the special structure of coronary articles. In this work, we investigate data manipulation strategies for training sequential CAG segmentation without labeled video data. We leveraged a general-purpose video segmentation model, XMem, for sequential CAG segmentation. We have proposed three data manipulation strategies for model training, including (1) sequence reversal for CAG data; (2) data hybridization for model training; (3) pseudo labels for sequential CAG data. The experimental results show that with good training strategies, one can use general-purpose video segmentation networks for sequential CAG data without explicitly labeled data. The best result of our trained network can achieve an average F1 score of 85.04% and an average region similarity of 73.97%, both of which are higher than the state-of-the-art results.