# Defining the boundaries: challenges and advances in identifying cells in microscopy images

arXiv: https://arxiv.org/abs/2311.08269 · HTML: https://mlsanity.com/paper/2311.08269/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 78 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 21 of 21)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Medical Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2009.13120/) (2020)
- [UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2107.00781/) (2021)
- [The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions](https://mlsanity.com/paper/2308.05864/) (2023)
- [AnyStar: Domain randomized universal star-convex 3D instance segmentation](https://mlsanity.com/paper/2307.07044/) (2023)
- [Explainable AI for Bioinformatics: Methods, Tools, and Applications](https://mlsanity.com/paper/2212.13261/) (2022)
- [MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy](https://mlsanity.com/paper/2212.03465/) (2022)
- [Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data](https://mlsanity.com/paper/2110.15231/) (2021)
- [MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons](https://mlsanity.com/paper/2011.13228/) (2020)
- [ImJoy: an open-source computational platform for the deep learning era](https://mlsanity.com/paper/1905.13105/) (2019)
- [Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis](https://mlsanity.com/paper/2302.14762/) (2023)
- [A Hitchhiker`s Guide through the Bio-image Analysis Software Universe](https://mlsanity.com/paper/2204.07547/) (2022)
- [EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation](https://mlsanity.com/paper/2306.06152/) (2023)
- [Distributed-Something: scripts to leverage AWS storage and computing for distributed workflows at scale](https://mlsanity.com/paper/2210.01073/) (2022)

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

- [CellStyle: Improved Zero-Shot Cell Segmentation via Style Transfer](https://mlsanity.com/paper/2503.08603/) (2025)
- [MatPhaseBench: A Semantics-Guided Benchmark for Materials Phase Diagrams Understanding](https://mlsanity.com/paper/2607.02934/) (2026)

## Abstract (arXiv)

Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images. While improvements continue to be made in tools that rely on classical methods for segmentation, deep learning-based tools increasingly dominate advances in the technology. Specialist models such as Cellpose continue to improve in accuracy and user-friendliness, and segmentation challenges such as the Multi-Modality Cell Segmentation Challenge continue to push innovation in accuracy across widely-varying test data as well as efficiency and usability. Increased attention on documentation, sharing, and evaluation standards are leading to increased user-friendliness and acceleration towards the goal of a truly universal method.