Polar1DMLP: A Coronary Artery Lumen Segmentation Network in CCTA

As a reliable and non-invasive medical imaging method,Coronary Computed Tomography Angiography(CCTA) has been used to detect the stenoses and other lesions in coronary arteries.However,effective and automated CCTA imaging examination is based on precise coronary arteries lumen segmentation technology.The purpose of this study was to investigate a model which can get the high-quality 3D surface model of the coronary lumen.Here we proposed the deep learning based 1D Polar1MLP model,which can make good use of the complicated information of the coronary tree centerline information.We trained and evaluated our model with the publicly available Coronary Artery Stenoses Detection and Quantification Evaluation Framework(Rotterdam) including 78 coronary segments with experts'manual contour labels of them,and got the result with a Dice similarity coefficient of 0.796,mean surface distance(MSD) of 0.169 mm in the validation dataset with 16 segments.The result of the study indicated that the 1DPolarMLP model with consideration of the CT gray-level information and centerline guideline information,can predict more precise and smoother 3D surface model of the coronary.