Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD).However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which makes it di cult for blood vessels segmentation. In this paper, we propose a DBCU-Net for automatic segmentation of CAG images, which is an extension of U-Net, DenseNet with bidirectional convLSTM. The main contribution of our network is that instead of convolution in the feature extraction of U-Net, we incorporate dense connectivity and the bidirectional convLSTM to highlight salient features. We conduct our experiment on our private dataset, and achieve average Accuracy, Precision, Recall and F1-score for coronary artery segmentation of 0.985, 0.913, 0.847 and 0.879 respectively. IntroductionCoronary artery disease (CAD) is a common heart disease, which is the leading cause of death worldwide according to the World Health Organization (WHO) [1]. Globally, the number of patients with CAD is expected to increase from 327.9 million in 2017 to 365.9 million in 2026. CAD poses a huge threat to human life. An accurate diagnosis of CAD is particularly important. With the development of computer diagnosis and treatment technologies, more powerful medical methods, such as magnetic resonance imaging(MRI), computed tomography(CT) [2], and X-ray coronary angiography(CAG) [3], are proposed to assist diagnosis. As the "gold standard" for the diagnosis of CAD, CAG can precisely pinpoint the site and the degree of coronary artery stenosis, as well as the symptoms of the condition.Accurate medical image analysis is crucial to subsequent clinical diagnosis and treatment. Diagnosis and treatment of clinical diseases mainly rely on advanced instruments and doctors' high-precision technology. However, manual segmentation of such CAG images requires a lot of medical expertise,

Coronary angiography (CAG) is the “gold standard” for diagnosing coronary artery disease (CAD). However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which makes it difficult for blood vessels segmentation. In this paper, we propose a DBCU-Net for automatic segmentation of CAG images, which is an extension of U-Net, DenseNet with bidirectional convLSTM. The main contribution of our network is that instead of convolution in the feature extraction of U-Net, we incorporate dense connectivity and the bidirectional convLSTM to highlight salient features. We conduct our experiment on our private dataset, and achieve average Accuracy, Precision, Recall and F1-score for coronary artery segmentation of 0.985, 0.913, 0.847 and 0.879 respectively.

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