# Cell Detection with Star-convex Polygons

arXiv: https://arxiv.org/abs/1806.03535 · HTML: https://mlsanity.com/paper/1806.03535/

## Key facts

- Cited by 183 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2018.

## Built on by (most cited first, 30 of 183)

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- [MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy](https://mlsanity.com/paper/2212.03465/) (2022)
- [Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations](https://mlsanity.com/paper/2106.15918/) (2021)
- [InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation](https://mlsanity.com/paper/2408.15954/) (2024)
- [How Shift Equivariance Impacts Metric Learning for Instance Segmentation](https://mlsanity.com/paper/2101.05846/) (2021)
- [Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps](https://mlsanity.com/paper/2308.04526/) (2023)
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## Abstract (arXiv)

Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. Recent successful learning-based approaches include per-pixel cell segmentation with subsequent pixel grouping, or localization of bounding boxes with subsequent shape refinement. In situations of crowded cells, these can be prone to segmentation errors, such as falsely merging bordering cells or suppressing valid cell instances due to the poor approximation with bounding boxes. To overcome these issues, we propose to localize cell nuclei via star-convex polygons, which are a much better shape representation as compared to bounding boxes and thus do not need shape refinement. To that end, we train a convolutional neural network that predicts for every pixel a polygon for the cell instance at that position. We demonstrate the merits of our approach on two synthetic datasets and one challenging dataset of diverse fluorescence microscopy images.