Circle Representation for Medical Object Detection
This work addresses a domain-specific challenge in renal pathology by offering a more optimized detection method for medical objects, though it is incremental as it adapts existing detection frameworks.
The paper tackles the problem of detecting ball-shaped biomedical objects like glomeruli in medical images by proposing a circle representation instead of traditional bounding boxes, resulting in superior detection performance and improved rotation invariance.
Box representation has been extensively used for object detection in computer vision. Such representation is efficacious but not necessarily optimized for biomedical objects (e.g., glomeruli), which play an essential role in renal pathology. In this paper, we propose a simple circle representation for medical object detection and introduce CircleNet, an anchor-free detection framework. Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant. When detecting glomeruli and nuclei on pathological images, the proposed circle representation achieved superior detection performance and be more rotation-invariant, compared with the bounding box. The code has been made publicly available: https://github.com/hrlblab/CircleNet