PyJAMAS: open-source, multimodal segmentation and analysis of microscopy images

Fernandez-Gonzalez R., Balaghi N., Wang K., Hawkins R., Rothenberg K., Mcfaul C., ...More

Bioinformatics, vol.38, no.2, pp.594-596, 2022 (Scopus) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1093/bioinformatics/btab589
  • Journal Name: Bioinformatics
  • Journal Indexes: Scopus
  • Page Numbers: pp.594-596
  • TED University Affiliated: No


© 2021 The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: Our increasing ability to resolve fine details using light microscopy is matched by an increasing need to quantify images in order to detect and measure phenotypes. Despite their central role in cell biology, many image analysis tools require a financial investment, are released as proprietary software, or are implemented in languages not friendly for beginners, and thus are used as black boxes. To overcome these limitations, we have developed PyJAMAS, an open-source tool for image processing and analysis written in Python. PyJAMAS provides a variety of segmentation tools, including watershed and machine learning-based methods; takes advantage of Jupyter notebooks for the display and reproducibility of data analyses; and can be used through a cross-platform graphical user interface or as part of Python scripts via a comprehensive application programming interface.