IEEE Trans Med Imaging. 2022 Mar;41(3):746-754. doi: 10.1109/TMI.2021.3122835. Epub 2022 Mar 2.
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.
盒子表示法在计算机视觉中的目标检测中得到了广泛的应用。这种表示方法是有效的，但不一定针对生物医学对象（例如肾小球）进行了优化，肾小球在肾脏病理学中起着至关重要的作用。在本文中，我们提出了一种简单的圆形表示法来进行医学对象检测，并介绍了 CircleNet，这是一种无锚点检测框架。与传统的边界框表示法相比，所提出的边界圆表示法在三个方面进行了创新：（1）它针对球形生物医学对象进行了优化；（2）与边界框相比，圆表示法减少了自由度；（3）它自然具有更高的旋转不变性。在对病理图像中的肾小球和细胞核进行检测时，与边界框相比，所提出的圆形表示法具有更好的检测性能和更高的旋转不变性。该代码已在 https://github.com/hrlblab/CircleNet 上公开。
