Circle Representation for Medical Instance Object Segmentation
<|reference_start|>Circle Representation for Medical Instance Object Segmentation: Recently, circle representation has been introduced for medical imaging, designed specifically to enhance the detection of instance objects that are spherically shaped (e.g., cells, glomeruli, and nuclei). Given its outstanding effectiveness in instance detection, it is compelling to consider the application of circle representation for segmenting instance medical objects. In this study, we introduce CircleSnake, a simple end-to-end segmentation approach that utilizes circle contour deformation for segmenting ball-shaped medical objects at the instance level. The innovation of CircleSnake lies in these three areas: (1) It substitutes the complex bounding box-to-octagon contour transformation with a more consistent and rotation-invariant bounding circle-to-circle contour adaptation. This adaptation specifically targets ball-shaped medical objects. (2) The circle representation employed in CircleSnake significantly reduces the degrees of freedom to two, compared to eight in the octagon representation. This reduction enhances both the robustness of the segmentation performance and the rotational consistency of the method. (3) CircleSnake is the first end-to-end deep instance segmentation pipeline to incorporate circle representation, encompassing consistent circle detection, circle contour proposal, and circular convolution in a unified framework. This integration is achieved through the novel application of circular graph convolution within the context of circle detection and instance segmentation. In practical applications, such as the detection of glomeruli, nuclei, and eosinophils in pathological images, CircleSnake has demonstrated superior performance and greater rotation invariance when compared to benchmarks. The code has been made publicly available: https://github.com/hrlblab/CircleSnake.<|reference_end|>
@article{xiong2024circle,
    title={Circle Representation for Medical Instance Object Segmentation},
    author={Juming Xiong, Ethan H. Nguyen, Yilin Liu, Ruining Deng, Regina N
  Tyree, Hernan Correa, Girish Hiremath, Yaohong Wang, Haichun Yang, Agnes B.
  Fogo, and Yuankai Huo},
    journal={arXiv preprint arXiv:2403.11507},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11507},
    primaryClass={cs.CV}
}