Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Research Briefing
  • Published:

Creating a universal cell segmentation algorithm

Cell segmentation currently involves the use of various bespoke algorithms designed for specific cell types, tissues, staining methods and microscopy technologies. We present a universal algorithm that can segment all kinds of microscopy images and cell types across diverse imaging protocols.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Diversity of microscopy images in the challenge dataset for assessing cell segmentation algorithms.


  1. Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019). A review article that highlights the great potential of deep learning for cell segmentation in microscopy images.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Stringer, C. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021). This paper presents a generalist model for cell segmentation.

    Article  CAS  PubMed  Google Scholar 

  3. Caicedo, J. C. et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat. Methods 16, 1247–1253 (2019). This paper demonstrates that international competition is an effective way to solve challenging biological tasks.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Maška, M. et al. The Cell Tracking Challenge: 10 years of objective benchmarking. Nat. Methods 20, 1010–1020 (2023). This paper summarizes the challenges of cell segmentation and tracking.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Ma, J. & Wang, B. Towards foundation models of biological image segmentation. Nat. Methods 20, 953–955 (2023). This commentary article describes a blueprint for developing foundation models for bioimage segmentation.

    Article  CAS  PubMed  Google Scholar 

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Ma, J. et al. The multimodality cell segmentation challenge: toward universal solutions. Nat. Methods (2024).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Creating a universal cell segmentation algorithm. Nat Methods (2024).

Download citation

  • Published:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing