Segmenting 3D geometry of left coronary artery from coronary CT angiography using deep learning for hemodynamic evaluation.

ABSTRACT

While coronary CT angiography (CCTA) is crucial for detecting several coronary artery diseases, it fails to provide essential hemodynamic parameters for early detection and treatment. These parameters can be easily obtained by performing computational fluid dynamic (CFD) analysis on the 3D artery geometry generated by CCTA image segmentation. As the coronary artery is small in size, manually segmenting the left coronary artery from CCTA scans is a laborious, time-intensive, error-prone, and complicated task which also requires a high level of expertise. Academics recently proposed various automated segmentation techniques for combatting these issues. To further aid in this process, we present CoronarySegNet, a deep learning-based framework, for autonomous and accurate segmentation as well as generation of 3D geometry of the left coronary artery. The design is based on the original U-net topology and includes channel-aware attention blocks as well as deep residual blocks with spatial dropout that contribute to feature map independence by eliminating 2D feature maps rather than individual components. We trained, tested, and statistically evaluated our model using CCTA images acquired from various medical centers across Bangladesh and the Rotterdam Coronary Artery Algorithm Evaluation challenge dataset to improve generality. In empirical assessment, CoronarySegNet outperforms several other cutting-edge segmentation algorithms, attaining dice similarity coefficient of 0.78 on an average while being highly significant (p < 0.05). Additionally, both the 3D geometries generated by machine learning and semi-automatic method were statistically similar. Moreover, hemodynamic evaluation performed on these 3D geometries showed comparable results.