Overview: Intravascular ultrasound (IVUS) is an important imaging modality for diagnosing cardiovascular diseases. The annotation of major anatomical structures of blood vessels in IVUS images can provide necessary clinical parameters for lesion severity assessment, which is a necessary step for physicians' diagnosis. However, manual annotation is laborious and inefficient. With the development of deep learning, convolutional neural networks perform well in this task and are able to achieve automatic and accurate segmentation and recognition of the main anatomical structures of blood vessels. Existing IVUS image segmentation networks are mostly based on pixel-by-pixel prediction, which lacks overall constraints on the main structures of blood vessels and cannot guarantee that the topological relationships between main vessel structures conform to medical prior knowledge, which has a negative impact on the calculation of clinical parameters. To solve this problem, this paper proposes an IVUS image segmentation method based on polar coordinate modeling and a dense-distance regression network. First, a prior knowledge-based polar coordinate modeling is designed for encoding the two-dimensional mask of the main structure of blood vessels containing prior knowledge into a one-dimensional distance vector to avoid the topological relationship of the blood vessel structure from generating random changes in the network prediction. A dense-distance regression network consisting of a residual network and a semantic embedding branching module is then constructed for learning the mapping relationship between IVUS images and 1D distance vectors. To effectively constrain the learning direction of the network, a joint loss function is proposed. This loss function takes into account the actual spatial relationship between one-dimensional distance vectors and has a stronger supervisory capability. The network prediction results are finally reconstructed as a two-dimensional mask by spline curve fitting. The proposed method is validated on a 20 MHz IVUS image dataset. The experimental results show that the proposed method achieves 100% topology preservation in the media, lumen, and plaque regions and achieves the Jaccard measure (JM) of 0.89, 0.87, and 0.74, respectively. The advantage of the algorithm in this paper is that it can provide a high accuracy and topologically correct segmentation results of the vessel structures, which is suitable for general IVUS image segmentation. The clinical parameters provided are reliable and can be used as an important reference basis for physicians' diagnosis, reducing physicians' workload and improving diagnostic efficiency, which has a promising future in clinical applications.Aiming at the problem that existing intravascular ultrasound (IVUS) image segmentation networks cannot guarantee that the topological relationships between segmentation results conform to medical prior knowledge, which has a negative impact on clinical parameter calculation, an IVUS image segmentation method based on polar coordinate modeling and dense-distance regression network is proposed. This method converts two-dimensional (2D) masks to one-dimensional (1D) distance vectors to preserve the topology of the vessel structures through polar coordinate modeling with prior knowledge. Then a dense-distance regression network consisting of a residual network and semantic embedding branch is constructed for learning the mapping relationships between IVUS images and 1D distance vectors. A joint loss function is proposed to constrain the network learning direction. The prediction results are finally reconstructed as 2D masks by spline curve fitting. The experimental results show that the proposed method achieves 100% topology preservation in the media, lumen, and plaque regions, and achieves Jaccard measure (JM) of 0.89, 0.87, and 0.74, respectively. The algorithm is suitable for general IVUS image segmentation, with high accuracy, and can provide reliable clinical parameters.