Abstract
This paper addressed the vessel segmentation and disease diagnostic in coronary angiography image and proposed an Encoder-Decoder architecture of deep learning with End-to-End model, where Encoder is based on ResNet, and the deep features are exacted automatically, and the Decoder produces the segmentation result by balanced cross-entropy cost function. Furthermore, batch normalization is employed to decrease the gradient vanishing in the training process, so as to reduce the difficulty of training the deep neural network. The experiment results show that the algorithm effectively exacts the feature and edge information, therefore the complex background disturbance is suppressed convincingly, and the vessel segmentation precision is improved effectively, the segmentation precision for three typical vessels are 0.8365, 0.8924 and 0.6297 respectively; and the F-measure are 0.8514, 0.8786 and 0.7298, respectively. In addition, the experiment results show that our proposed can be generalized to the angiography image within limits.
Highlights
Cardiovascular disease is very common and one of the diseases with the highest morbidity in the world [1]
We use the coronary angiography image database as the object to carry out verification experiments
The abovementioned deep neural network models are used for segmentation experiments, and the experimental results were compared and analyzed, where the angiography image shown in Fig. 8 contains a complicated background
Summary
Cardiovascular disease is very common and one of the diseases with the highest morbidity in the world [1]. This paper builds Encoder-Decode framework based on deep residual network (ResNet), completes the automatic extraction of image features and the learning of segmentation models, achieving end-to-end segmentation result from input image to the segmentation result. THE GENERAL FRAMEWORK Deep learning is to train a large number of samples to make the trained deep neural network approach the real model Is = f (I ) without intermediate process, so that the end-to-end task mode from the input image to the segmentation result can be realized [18].
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