# Circle Representation for Medical Instance Object Segmentation

arXiv: https://arxiv.org/abs/2403.11507 · HTML: https://mlsanity.com/paper/2403.11507/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Hybrid Task Cascade for Instance Segmentation](https://mlsanity.com/paper/1901.07518/) (2019)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [Deep Snake for Real-Time Instance Segmentation](https://mlsanity.com/paper/2001.01629/) (2020)
- [YOLACT++: Better Real-time Instance Segmentation](https://mlsanity.com/paper/1912.06218/) (2019)
- [MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features](https://mlsanity.com/paper/1712.04837/) (2017)
- [Nuclei instance segmentation and classification in histopathology images with StarDist](https://mlsanity.com/paper/2203.02284/) (2022)
- [EmbedMask: Embedding Coupling for One-stage Instance Segmentation](https://mlsanity.com/paper/1912.01954/) (2019)
- [Circle Representation for Medical Object Detection](https://mlsanity.com/paper/2110.12093/) (2021)
- [SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching](https://mlsanity.com/paper/2104.05215/) (2021)
- [CNN Cascades for Segmenting Whole Slide Images of the Kidney](https://mlsanity.com/paper/1708.00251/) (2017)
- [YolactEdge: Real-time Instance Segmentation on the Edge](https://mlsanity.com/paper/2012.12259/) (2020)
- [CircleSnake: Instance Segmentation with Circle Representation](https://mlsanity.com/paper/2211.01254/) (2022)
- [Deep Learning-Based Open Source Toolkit for Eosinophil Detection in Pediatric Eosinophilic Esophagitis](https://mlsanity.com/paper/2308.06333/) (2023)
- [Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation](https://mlsanity.com/paper/2308.08974/) (2023)
- [CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and Attention](https://mlsanity.com/paper/2308.16145/) (2023)

## Built on by (most cited first, 3 of 3)

- [Weighted Circle Fusion: Ensembling Circle Representation from Different Object Detection Results](https://mlsanity.com/paper/2406.19540/) (2024)
- [ZeroReg3D: A Zero-shot Registration Pipeline for 3D Consecutive Histopathology Image Reconstruction](https://mlsanity.com/paper/2506.21923/) (2025)
- [Expanding Training Data for Endoscopic Phenotyping of Eosinophilic Esophagitis](https://mlsanity.com/paper/2502.04199/) (2025)

## Abstract (arXiv)

Recently, circle representation has been introduced for medical imaging, designed specifically to enhance the detection of instance objects that are spherically shaped (e.g., cells, glomeruli, and nuclei). Given its outstanding effectiveness in instance detection, it is compelling to consider the application of circle representation for segmenting instance medical objects. In this study, we introduce CircleSnake, a simple end-to-end segmentation approach that utilizes circle contour deformation for segmenting ball-shaped medical objects at the instance level. The innovation of CircleSnake lies in these three areas: (1) It substitutes the complex bounding box-to-octagon contour transformation with a more consistent and rotation-invariant bounding circle-to-circle contour adaptation. This adaptation specifically targets ball-shaped medical objects. (2) The circle representation employed in CircleSnake significantly reduces the degrees of freedom to two, compared to eight in the octagon representation. This reduction enhances both the robustness of the segmentation performance and the rotational consistency of the method. (3) CircleSnake is the first end-to-end deep instance segmentation pipeline to incorporate circle representation, encompassing consistent circle detection, circle contour proposal, and circular convolution in a unified framework. This integration is achieved through the novel application of circular graph convolution within the context of circle detection and instance segmentation. In practical applications, such as the detection of glomeruli, nuclei, and eosinophils in pathological images, CircleSnake has demonstrated superior performance and greater rotation invariance when compared to benchmarks. The code has been made publicly available: https://github.com/hrlblab/CircleSnake.