Coronary Artery Vascular Segmentation on Limited Data via Pseudo-Precise Label

The scale of training data is significant in segmentation task, especially in segmenting the medical coronary artery angiograms. Traditional semantic segmentation networks have been restricted in this field, due to the particularity of cardiac coronary angiography data, that is, it is very difficult to balance the manual labeling costs and network accuracy. On the basis of these observations, we propose a new method to generate the so-called ‘pseudo-precise’ label and a complementary training pipeline, which can improve the performance of the networks on the premise of reducing labor costs as much as possible. Our method can thus increase the f1-score by 4%-11% with the same amount of precisely labeled data.