OpSeF: Open Source Python Framework for Collaborative Instance Segmentation of Bioimages.

Area of Science:

• Bioimage analysis
• Computational biology
• Machine learning in life sciences

Background:

• Deep learning models for bioimage segmentation are available but individually testing them is difficult.
• There is a need for a unified framework to streamline the use and collaboration for bioimage analysis.

Purpose of the Study:

• Introduce the Open Segmentation Framework (OpSeF) for deep learning-based instance segmentation of bioimages.
• Facilitate collaboration between biomedical users and image analysts for complex, reproducible analysis.
• Enable efficient benchmarking and optimization of segmentation models.

Main Methods:

• OpSeF is a Python framework integrating pre-trained deep learning models for bioimage segmentation.
• It semi-automates preprocessing, segmentation (2D/3D), and postprocessing.
• Includes standard inputs/outputs for modularity and interoperability, with integrated U-Net, StarDist, and Cellpose.

Main Results:

• OpSeF facilitates reproducible, well-documented, and scalable analysis workflows.
• It streamlines parameter optimization, often allowing use of pre-trained models without retraining.
• Provides intermediate results to guide selection of optimal CNN architectures and manual data labeling if needed.

Conclusions:

• OpSeF enhances collaboration and efficiency in bioimage analysis by leveraging deep learning.
• The framework supports benchmarking, optimization, and the integration of new models.
• OpSeF has the potential to become a central repository for sharing and reusing pre-trained bioimage segmentation models.