# PseudoEdgeNet: Nuclei Segmentation only with Point Annotations

arXiv: https://arxiv.org/abs/1906.02924 · HTML: https://mlsanity.com/paper/1906.02924/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 27 of 27)

- [Multi-Class Cell Detection Using Spatial Context Representation](https://mlsanity.com/paper/2110.04886/) (2021)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training](https://mlsanity.com/paper/2202.08195/) (2022)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model](https://mlsanity.com/paper/2304.03572/) (2023)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Weakly Supervised Nuclei Segmentation via Instance Learning](https://mlsanity.com/paper/2202.01564/) (2022)
- [BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels](https://mlsanity.com/paper/2401.07437/) (2024)
- [DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions](https://mlsanity.com/paper/2404.14956/) (2024)
- [Topology-Guided Multi-Class Cell Context Generation for Digital Pathology](https://mlsanity.com/paper/2304.02255/) (2023)
- [SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images](https://mlsanity.com/paper/2106.13556/) (2021)
- [Weakly Supervised Attended Object Detection Using Gaze Data as Annotations](https://mlsanity.com/paper/2204.07090/) (2022)
- [Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization](https://mlsanity.com/paper/2103.05617/) (2021)
- [Annotation by Clicks: A Point-Supervised Contrastive Variance Method for Medical Semantic Segmentation](https://mlsanity.com/paper/2212.08774/) (2022)
- [Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models](https://mlsanity.com/paper/2404.13239/) (2024)
- [Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review](https://mlsanity.com/paper/2208.12460/) (2022)
- [Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images](https://mlsanity.com/paper/2210.07862/) (2022)
- [Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization](https://mlsanity.com/paper/2306.14136/) (2023)
- [Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting](https://mlsanity.com/paper/2511.19953/) (2025)
- [A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation](https://mlsanity.com/paper/2110.13846/) (2021)
- [Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images](https://mlsanity.com/paper/2208.00098/) (2022)
- [Label-efficient Contrastive Learning-based model for nuclei detection and classification in 3D Cardiovascular Immunofluorescent Images](https://mlsanity.com/paper/2309.03744/) (2023)
- [BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks](https://mlsanity.com/paper/2206.04558/) (2022)
- [Application of attention-based Siamese composite neural network in medical image recognition](https://mlsanity.com/paper/2304.09783/) (2023)
- [A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention](https://mlsanity.com/paper/2407.18673/) (2024)
- [Leveraging Weak Supervision for Cell Localization in Digital Pathology Using Multitask Learning and Consistency Loss](https://mlsanity.com/paper/2412.15392/) (2024)
- [LG-NuSegHop: A Local-to-Global Self-Supervised Pipeline For Nuclei Instance Segmentation](https://mlsanity.com/paper/2511.04892/) (2025)

## Abstract (arXiv)

Nuclei segmentation is one of the important tasks for whole slide image analysis in digital pathology. With the drastic advance of deep learning, recent deep networks have demonstrated successful performance of the nuclei segmentation task. However, a major bottleneck to achieving good performance is the cost for annotation. A large network requires a large number of segmentation masks, and this annotation task is given to pathologists, not the public. In this paper, we propose a weakly supervised nuclei segmentation method, which requires only point annotations for training. This method can scale to large training set as marking a point of a nucleus is much cheaper than the fine segmentation mask. To this end, we introduce a novel auxiliary network, called PseudoEdgeNet, which guides the segmentation network to recognize nuclei edges even without edge annotations. We evaluate our method with two public datasets, and the results demonstrate that the method consistently outperforms other weakly supervised methods.