Coronary Artery Centerline Tracking Method Based On Convolutional And Recurrent Neural Networks

Coronary artery centerline extraction is of great significance for the diagnosis and quantitative analysis of cardiovascular diseases.However,due to the complex coronary structure,large difference in branches structure among different individuals,as well as noise and artifacts in medical images,there are still some drawbacks in the current algorithm of coronary artery centerline extraction,such as the need for manual interaction,the complicated image preprocessing,the unsatisfied extraction on lesions,and the difficulty in extracting branch centerlines.To solves these problems,we propose a coronary artery centerline tracking method which combines convolutional neural network and recurrent neural network in CCTA images.The main work of this thesis is given as follows:(1)Detection of the seed points of the coronary artery centerline based on a two-pathway fully convolutional network.In the algorithm of the coronary artery centerline tracking,how to find the location of seeds point is very important.However,the existing detection methods of seed points have some shortcomings,such as the need of prior knowledge,complicated detection process,and poor detection performance.Therefore,we propose an automatic method to detect the starting seed points of the coronary artery centerline.We segment the coronary artery centerline by Deep Medic,a two-pathway fully convolutional network,and then take the discrete centerline points as seed points with the ‘under-segmentation' results.The experimental results show that the seed points obtained by our method are widely distributed and highly coincident with the reference centerline.In the comparative experiment,the Dice coefficient of the Deep Medic network segmentation is 0.3003,and the Dice coefficients of the U-Net and V-Net are 0.2176 and 0.2395 respectively,which proves that the detection performance of Deep Medic is better than the contrasted methods.(2)Coronary artery centerline tracking based on convolution and recurrent neural networks.In the current centerline tracking algorithm,the determination of the centerline points only calculates the local features in the current image area,and does not take the wider range of context information into account.Therefore,we design a centerline tracking network model that combines convolutional neural network and recurrent neural network.During centerline tracking process,local information and global information are combined to track the centerline.The stop condition is formulated based on the probability entropy value.The tracking starts from the seed points detected by Deep Medic,and finally achieve the extraction of the complete coronary artery centerline.We use the Rotterdam coronary artery algorithm evaluation framework to evaluate our method.The experimental results show that the total overlap,the overlap until first error,the Overlap with the clinically relevant part of the vessel,and the average inside accuracy metric reached 91.2%,78.9%,93.3% and 0.24 mm respectively,and only need 20 s to obtain a complete coronary artery centerline in the test image.Compared with other centerline extraction methods,our method has good performance in accuracy,robustness and time consumption.