Deep Convolutional Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation

X-ray coronary angiography is a crucial clinical procedure for the diagnosis and treatment of coronary artery disease, which accounts for roughly 16\% of global deaths every year. However, the images acquired in this procedure have low resolution and poor contrast, making lesion detection and assessment challenging. Accurate coronary artery segmentation not only helps mitigate these problems, but also allows the extraction of relevant anatomical features for further analysis by quantitative methods. Although automated segmentation of coronary arteries has been proposed before, previous approaches have used non-optimal segmentation criteria, leading to less useful results. Most methods either segment only the major vessel, discarding important information from the remaining ones, or segment the whole coronary tree, based mostly on contrast information, producing a noisy output that includes vessels that are not relevant for diagnostic nor therapeutic purposes. In this work, vessels are segmented according to their clinical relevance, using a segmentation criterion developed in collaboration with expert cardiologists. Additionally, the catheter, whose diameter is known and provides a scale factor that may be useful for diagnosis, is segmented simultaneously. To derive the optimal approach, an extensive comparative study of encoder-decoder architectures was conducted. Based on the UNet++, a new computationally efficient and high-performing decoder architecture is proposed, the EfficientUNet++. Combined with EfficientNet encoders, the EfficientUNet++ establishes a line of efficient and high-performing segmentation models, whose best-performing member achieves a generalized dice score of 0.9202 +/- 0.0356, and artery and catheter class dice scores of 0.8858 +/- 0.0461 and 0.7627 +/- 0.1812, respectively.