Circle Representation for Medical Instance Object Segmentation
Juming Xiong Ethan H. Nguyen Yilin Liu Ruining Deng Regina N Tyree Hernan Correa Girish Hiremath Yaohong Wang Haichun Yang Agnes B. Fogo Yuankai Huo
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.