# CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation

arXiv: https://arxiv.org/abs/2102.06867 · HTML: https://mlsanity.com/paper/2102.06867/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 62 references that are in the map.
- First posted on arXiv 13 Feb 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [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)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation](https://mlsanity.com/paper/1604.02677/) (2016)
- [Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation](https://mlsanity.com/paper/2007.02846/) (2020)
- [Micro-Net: A unified model for segmentation of various objects in microscopy images](https://mlsanity.com/paper/1804.08145/) (2018)
- [CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation](https://mlsanity.com/paper/1903.05358/) (2019)
- [Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis](https://mlsanity.com/paper/2002.08725/) (2020)
- [Mask-RCNN and U-net Ensembled for Nuclei Segmentation](https://mlsanity.com/paper/1901.10170/) (2019)
- [Regularized Densely-connected Pyramid Network for Salient Instance Segmentation](https://mlsanity.com/paper/2008.12416/) (2020)
- [Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes](https://mlsanity.com/paper/1907.09140/) (2019)
- [Boundary-assisted Region Proposal Networks for Nucleus Segmentation](https://mlsanity.com/paper/2006.02695/) (2020)
- [Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints](https://mlsanity.com/paper/2004.09821/) (2020)
- [Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework](https://mlsanity.com/paper/1908.10356/) (2019)
- [PatchPerPix for Instance Segmentation](https://mlsanity.com/paper/2001.07626/) (2020)
- [MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons](https://mlsanity.com/paper/2011.13228/) (2020)

## Built on by (most cited first, 30 of 32)

- [CellViT: Vision Transformers for Precise Cell Segmentation and Classification](https://mlsanity.com/paper/2306.15350/) (2023)
- [Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation](https://mlsanity.com/paper/2401.13220/) (2024)
- [UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images](https://mlsanity.com/paper/2402.16663/) (2024)
- [Unleashing the Power of Prompt-driven Nucleus Instance Segmentation](https://mlsanity.com/paper/2311.15939/) (2023)
- [Structure Embedded Nucleus Classification for Histopathology Images](https://mlsanity.com/paper/2302.11416/) (2023)
- [PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification](https://mlsanity.com/paper/2111.01557/) (2021)
- [Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation](https://mlsanity.com/paper/2401.09496/) (2024)
- [SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation](https://mlsanity.com/paper/2401.09773/) (2024)
- [NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation](https://mlsanity.com/paper/2408.11787/) (2024)
- [Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment](https://mlsanity.com/paper/2411.00078/) (2024)
- [MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2507.19931/) (2025)
- [ConvNeXt-backbone HoVerNet for nuclei segmentation and classification](https://mlsanity.com/paper/2202.13560/) (2022)
- [Enhancing Nucleus Segmentation with HARU-Net: A Hybrid Attention Based Residual U-Blocks Network](https://mlsanity.com/paper/2308.03382/) (2023)
- [Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation](https://mlsanity.com/paper/2402.16280/) (2024)
- [LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels](https://mlsanity.com/paper/2407.18054/) (2024)
- [The Four Color Theorem for Cell Instance Segmentation](https://mlsanity.com/paper/2506.09724/) (2025)
- [Co-Seg++: Mutual Prompt-Guided Collaborative Learning for Versatile Medical Segmentation](https://mlsanity.com/paper/2506.17159/) (2025)
- [Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation](https://mlsanity.com/paper/2509.06740/) (2025)
- [NuLite -- Lightweight and Fast Model for Nuclei Instance Segmentation and Classification](https://mlsanity.com/paper/2408.01797/) (2024)
- [EM3M: An Electron Micrograph Dataset for Microstructural Segmentation and Generation](https://mlsanity.com/paper/2508.16239/) (2025)
- [Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting](https://mlsanity.com/paper/2511.19953/) (2025)
- [Disco: Densely-overlapping Cell Instance Segmentation via Adjacency-aware Collaborative Coloring](https://mlsanity.com/paper/2602.05420/) (2026)
- [NuNext: Reframing Nucleus Detection as Next-Point Detection](https://mlsanity.com/paper/2603.07098/) (2026)
- [UniCell: Universal Cell Nucleus Classification via Prompt Learning](https://mlsanity.com/paper/2402.12938/) (2024)
- [Learning Physical Dynamics for Object-centric Visual Prediction](https://mlsanity.com/paper/2403.10079/) (2024)
- [AMA-SAM: Adversarial Multi-Domain Alignment of Segment Anything Model for High-Fidelity Histology Nuclei Segmentation](https://mlsanity.com/paper/2503.21695/) (2025)
- [LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images](https://mlsanity.com/paper/2601.03163/) (2026)
- [Skeleton-Guided Sparse Anchors for Rotated Instance Segmentation in Cell Microscopy](https://mlsanity.com/paper/2401.09895/) (2024)
- [A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention](https://mlsanity.com/paper/2407.18673/) (2024)
- [Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement](https://mlsanity.com/paper/2603.28027/) (2026)

## Abstract (arXiv)

Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate between touching and overlapping nuclei and have accordingly achieved promising performance. Each polygon is represented by a set of centroid-to-boundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus. However, using the centroid pixel alone does not provide sufficient contextual information for robust prediction and thus degrades the segmentation accuracy. To handle this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than one single pixel within each cell for distance prediction. This strategy substantially enhances contextual information and thereby improves the robustness of the prediction. Second, we propose a Confidence-based Weighting Module, which adaptively fuses the predictions from the sampled point set. Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains the shape of the predicted polygons. Here, the SAP loss is based on an additional network that is pre-trained by means of mapping the centroid probability map and the pixel-to-boundary distance maps to a different nucleus representation. Extensive experiments justify the effectiveness of each component in the proposed CPP-Net. Finally, CPP-Net is found to achieve state-of-the-art performance on three publicly available databases, namely DSB2018, BBBC06, and PanNuke. Code of this paper is available at urlhttps://github.com/csccsccsccsc/cpp-net