BioImageIT

BioImageIT: Streamline Your Bioimage Analysis & Make it FAIR

BioImageIT is a workflow management system designed to drastically simplify the creation, execution, and duplication of data science experiments, with specialized features tailored for image analysis. Our primary goal is to make bioimage analysis truly FAIR: Findable, Accessible, Interoperable, and Reproducible.

With BioImageIT, you can:

  • Build Workflows Your Way: Whether you prefer a visual, node-based graphical interface or coding in Python, BioImageIT offers flexible options for pipeline creation.

  • Forget Dependency Headaches: Access a wide array of tools (Python, Java, C++, R) that run in isolated environments. BioImageIT handles the setup and installation automatically, ensuring tools work without conflicts.

  • Integrate & Extend Easily: Bring your own tools into the framework or create new ones quickly using our provided Python template.

  • Visualize Complex Data: Explore multi-dimensional images (3D+t) seamlessly with built-in Napari integration.

  • Manage Data Effectively: Leverage the power of Pandas for data handling within your workflows and connect directly to Omero repositories.

  • Boost Performance: Speed up your analysis with automatic parallel processing of workflow steps (cluster computing support coming soon).

  • Get Started Instantly: Install with a simple double-click and manage updates effortlessly.

BioImageIT lets you focus on scientific discovery by making powerful bioimage analysis accessible, manageable, and truly reproducible.

This project currently heavily relies on PyFlow.

BioImageIT was made possible thanks to omero-py, pandas, Qt, Conda, and many others.

BioImageIT is tested on Windows (x86_64 and arm64), macOS (x86_64 and arm64) and Linux (x86_64) operating systems.

Overview

Open science and FAIR principles are major topics in the field of modern microscopy for biology. This is due to both new data acquisition technologies like super-resolution and light sheet microscopy that generate large datasets but also to the new data analysis methodologies such as deep learning that automate data mining with high accuracy. Nevertheless data are still rarely shared and annotated because this implies a lot of manual and tedious work and software packaging. We present BioImageIT an open source framework that integrates automation of image data management with data processing. Scientists then only need to import their data once in BioImageIT, which automatically generates and manages the metadata every time an operation is performed on the data. This accelerates the data mining process with no need any more to deal with IT integration and manual analysis and annotations required to build training sets for machine learning techniques. BioImageIT then automatically implements FAIR principles. The interest of bioImageIT is thus twofold.