# Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response

arXiv: https://arxiv.org/abs/1911.13077 · HTML: https://mlsanity.com/paper/1911.13077/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2019.

## Built on by (most cited first, 17 of 17)

- [Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings](https://mlsanity.com/paper/2103.14572/) (2021)
- [Weakly Supervised Nuclei Segmentation via Instance Learning](https://mlsanity.com/paper/2202.01564/) (2022)
- [DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions](https://mlsanity.com/paper/2404.14956/) (2024)
- [Co-synthesis of Histopathology Nuclei Image-Label Pairs using a Context-Conditioned Joint Diffusion Model](https://mlsanity.com/paper/2407.14434/) (2024)
- [Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection](https://mlsanity.com/paper/2303.05269/) (2023)
- [DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images](https://mlsanity.com/paper/2410.03058/) (2024)
- [Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification](https://mlsanity.com/paper/2107.09289/) (2021)
- [Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing](https://mlsanity.com/paper/2304.10671/) (2023)
- [Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap](https://mlsanity.com/paper/2107.08653/) (2021)
- [Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization](https://mlsanity.com/paper/2306.14136/) (2023)
- [Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency](https://mlsanity.com/paper/2107.08639/) (2021)
- [Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images](https://mlsanity.com/paper/2208.00098/) (2022)
- [Adaptive Pseudo Label Selection for Individual Unlabeled Data by Positive and Unlabeled Learning](https://mlsanity.com/paper/2508.07548/) (2025)
- [Transformer Assisted Convolutional Network for Cell Instance Segmentation](https://mlsanity.com/paper/2110.02270/) (2021)
- [BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks](https://mlsanity.com/paper/2206.04558/) (2022)
- [Cell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise Detection](https://mlsanity.com/paper/2403.13412/) (2024)
- [Entropy Bootstrapping for Weakly Supervised Nuclei Detection](https://mlsanity.com/paper/2411.13528/) (2024)

## Abstract (arXiv)

Cell shape analysis is important in biomedical research. Deep learning methods may perform to segment individual cells if they use sufficient training data that the boundary of each cell is annotated. However, it is very time-consuming for preparing such detailed annotation for many cell culture conditions. In this paper, we propose a weakly supervised method that can segment individual cell regions who touch each other with unclear boundaries in dense conditions without the training data for cell regions. We demonstrated the efficacy of our method using several data-set including multiple cell types captured by several types of microscopy. Our method achieved the highest accuracy compared with several conventional methods. In addition, we demonstrated that our method can perform without any annotation by using fluorescence images that cell nuclear were stained as training data. Code is publicly available in "https://github.com/naivete5656/WSISPDR".