# Contour Proposal Networks for Biomedical Instance Segmentation

arXiv: https://arxiv.org/abs/2104.03393 · HTML: https://mlsanity.com/paper/2104.03393/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 22 of 22)

- [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)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [PolarMask: Single Shot Instance Segmentation with Polar Representation](https://mlsanity.com/paper/1909.13226/) (2019)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation](https://mlsanity.com/paper/1604.02677/) (2016)
- [Learning deep structured active contours end-to-end](https://mlsanity.com/paper/1803.06329/) (2018)
- [A Closer Look at Domain Shift for Deep Learning in Histopathology](https://mlsanity.com/paper/1909.11575/) (2019)
- [End to End Trainable Active Contours via Differentiable Rendering](https://mlsanity.com/paper/1912.00367/) (2019)
- [Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks](https://mlsanity.com/paper/2004.14010/) (2020)
- [Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells](https://mlsanity.com/paper/1802.07465/) (2018)
- [Straight to Shapes: Real-time Detection of Encoded Shapes](https://mlsanity.com/paper/1611.07932/) (2016)
- [Straight to Shapes++: Real-time Instance Segmentation Made More Accurate](https://mlsanity.com/paper/1905.11358/) (2019)

## Built on by (most cited first, 13 of 13)

- [The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions](https://mlsanity.com/paper/2308.05864/) (2023)
- [AnyStar: Domain randomized universal star-convex 3D instance segmentation](https://mlsanity.com/paper/2307.07044/) (2023)
- [Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain](https://mlsanity.com/paper/2311.16821/) (2023)
- [Automated Classification of Cell Shapes: A Comparative Evaluation of Shape Descriptors](https://mlsanity.com/paper/2411.00561/) (2024)
- [CytoNet: A Foundation Model for the Human Cerebral Cortex at Cellular Resolution](https://mlsanity.com/paper/2511.01870/) (2025)
- [ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass](https://mlsanity.com/paper/2106.03382/) (2021)
- [FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation](https://mlsanity.com/paper/2308.13790/) (2023)
- [AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy](https://mlsanity.com/paper/2310.04461/) (2023)
- [How To Make Your Cell Tracker Say "I dunno!"](https://mlsanity.com/paper/2503.09244/) (2025)
- [Cytoarchitecture in Words: Weakly Supervised Vision-Language Modeling for Human Brain Microscopy](https://mlsanity.com/paper/2602.23088/) (2026)
- [EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis](https://mlsanity.com/paper/2504.00047/) (2025)
- [From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging](https://mlsanity.com/paper/2505.11394/) (2025)
- [Boosting Overlapping Organoid Instance Segmentation Using Pseudo-Label Unmixing and Synthesis-Assisted Learning](https://mlsanity.com/paper/2601.06642/) (2026)

## Abstract (arXiv)

We present a conceptually simple framework for object instance segmentation called Contour Proposal Network (CPN), which detects possibly overlapping objects in an image while simultaneously fitting closed object contours using an interpretable, fixed-sized representation based on Fourier Descriptors. The CPN can incorporate state of the art object detection architectures as backbone networks into a single-stage instance segmentation model that can be trained end-to-end. We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities. In our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in instance segmentation accuracy, and present variants with execution times suitable for real-time applications. The trained models generalize well across different domains of cell types. Since the main assumption of the framework are closed object contours, it is applicable to a wide range of detection problems also outside the biomedical domain. An implementation of the model architecture in PyTorch is freely available.