# Single-shot Star-convex Polygon-based Instance Segmentation for Spatially-correlated Biomedical Objects

arXiv: https://arxiv.org/abs/2504.12078 · HTML: https://mlsanity.com/paper/2504.12078/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing](https://mlsanity.com/paper/1804.03287/) (2018)
- [Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey](https://mlsanity.com/paper/1507.01030/) (2015)
- [Lightweight Deep Learning for Resource-Constrained Environments: A Survey](https://mlsanity.com/paper/2404.07236/) (2024)
- [Robust and Decomposable Average Precision for Image Retrieval](https://mlsanity.com/paper/2110.01445/) (2021)
- [Optimizing Star-Convex Functions](https://mlsanity.com/paper/1511.04466/) (2015)
- [A Delay Metric for Video Object Detection: What Average Precision Fails to Tell](https://mlsanity.com/paper/1908.06368/) (2019)
- [SortedAP: Rethinking evaluation metrics for instance segmentation](https://mlsanity.com/paper/2309.04887/) (2023)

## Built on by (most cited first, 2 of 2)

- [End-to-end plaque counting and virus titration from laboratory plate images with deep learning](https://mlsanity.com/paper/2605.16008/) (2026)
- [Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI](https://mlsanity.com/paper/2607.12874/) (2026)

## Abstract (arXiv)

Biomedical images often contain objects known to be spatially correlated or nested due to their inherent properties, leading to semantic relations. Examples include cell nuclei being nested within eukaryotic cells and colonies growing exclusively within their culture dishes. While these semantic relations bear key importance, detection tasks are often formulated independently, requiring multi-shot analysis pipelines. Importantly, spatial correlation could constitute a fundamental prior facilitating learning of more meaningful representations for tasks like instance segmentation. This knowledge has, thus far, not been utilised by the biomedical computer vision community. We argue that the instance segmentation of two or more categories of objects can be achieved in parallel. We achieve this via two architectures HydraStarDist (HSD) and the novel (HSD-WBR) based on the widely-used StarDist (SD), to take advantage of the star-convexity of our target objects. HSD and HSD-WBR are constructed to be capable of incorporating their interactions as constraints into account. HSD implicitly incorporates spatial correlation priors based on object interaction through a joint encoder. HSD-WBR further enforces the prior in a regularisation layer with the penalty we proposed named Within Boundary Regularisation Penalty (WBR). Both architectures achieve nested instance segmentation in a single shot. We demonstrate their competitiveness based on IoUR and AP and superiority in a new, task-relevant criteria, Joint TP rate (JTPR) compared to their baseline SD and Cellpose. Our approach can be further modified to capture partial-inclusion/-exclusion in multi-object interactions in fluorescent or brightfield microscopy or digital imaging. Finally, our strategy suggests gains by making this learning single-shot and computationally efficient.