CircleNet: Anchor-free Glomerulus Detection with Circle Representation. Academic Article uri icon

Overview

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.

publication date

  • September 29, 2020

Identity

PubMed Central ID

  • PMC8372751

Scopus Document Identifier

  • 85092793764

Digital Object Identifier (DOI)

  • 10.1007/978-3-030-59719-1_4

PubMed ID

  • 34414404

Additional Document Info

volume

  • 2020