Yang Haichun, Deng Ruining, Lu Yuzhe, Zhu Zheyu, Chen Ye, Roland Joseph T, Lu Le, Landman Bennett A, Fogo Agnes B, Huo Yuankai
Vanderbilt University Medical Center, Nashville TN 37215, USA.
Vanderbilt University, Nashville TN 37215, USA.
Med Image Comput Comput Assist Interv. 2020;2020:35-44. doi: 10.1007/978-3-030-59719-1_4. Epub 2020 Sep 29.
Object detection networks are powerful in computer vision, but not necessarily optimized for biomedical object detection. In this work, we propose CircleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus. Different from the traditional bounding box based detection method, the bounding circle (1) reduces the degrees of freedom of detection representation, (2) is naturally rotation invariant, (3) and optimized for ball-shaped objects. The key innovation to enable this representation is the anchor-free framework with the circle detection head. We evaluate CircleNet in the context of detection of glomerulus. CircleNet increases average precision of the glomerulus detection from 0.598 to 0.647. Another key advantage is that CircleNet achieves better rotation consistency compared with bounding box representations.
目标检测网络在计算机视觉中功能强大,但不一定针对生物医学目标检测进行了优化。在这项工作中,我们提出了CircleNet,这是一种简单的无锚检测方法,采用圆形表示来检测球形肾小球。与传统的基于边界框的检测方法不同,边界圆(1)减少了检测表示的自由度,(2)具有自然的旋转不变性,(3)并且针对球形物体进行了优化。实现这种表示的关键创新在于带有圆形检测头的无锚框架。我们在肾小球检测的背景下评估了CircleNet。CircleNet将肾小球检测的平均精度从0.598提高到了0.647。另一个关键优势是,与边界框表示相比,CircleNet实现了更好的旋转一致性。