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PRNet: polar regression network for medical image segmentation

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Abstract

High-performance segmentation of medical images, though important for automatic clinical diagnosis, remains a challenging problem. In this paper, we propose a novel polar regression network (PRNet) for the segmentation of oval-shaped targets in medical images. Through polar representation, the segmentation problem is formulated and decomposed into two sub-problems of center map estimation and ray length regression. The center map estimation is supervised by the probability center map, which is pre-generated by the contour-based algorithm. The ray length regression further leverages the center-attention polar loss and projection distance loss, in order to emphasize pixels in high probability in the center mask and capitalize on the implicit shape information. Experimental results demonstrate the effectiveness of our approach on the segmentation of oval targets in medical images.

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Authors and Affiliations

  1. School of Software Engineering, East China Normal University, Shanghai, 200062, China

    Xiaoxiao Qian & Min Wu

  2. School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China

    Hongyan Quan

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  1. Xiaoxiao Qian
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  2. Hongyan Quan
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Correspondence to Hongyan Quan.

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Qian, X., Quan, H. & Wu, M. PRNet: polar regression network for medical image segmentation. Vis Comput 39, 87–98 (2023). https://doi.org/10.1007/s00371-021-02315-y

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