CircleNet: Anchor-free Detection with Circle Representation

Abstract

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.

References in corpus (2)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• New Descriptor for Glomerulus Detection in Kidney Microscopy Image

Cited by in corpus (4)

• SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching
• EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology
• Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide Images
• Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation