Abstract: 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.