QuPath: Boosting Bioimage Analysis for Users & Developers
Peter Bankhead (University of Edinburgh)
To enable researchers to more deeply interrogate complex biomedical images by improving the extensibility, robustness, and interoperability of QuPath.
By providing sophisticated quantitative analysis and artificial intelligence tools for histology, pathology, and multiplexed images, QuPath has raised the baseline for tissue image analysis and become an essential tool for many researchers. The team has identified the following improvements are needed: 1. Converting QuPath to become a modularized Java application, following the Java Platform Module System, with a stable and fully documented API to support script and extension writers 2. Providing thoroughly tested templates for scripts and extensions to lower the barrier for other developers wishing to add new functionality 3. Improving interoperability, through adopting community-driven standards (e.g. Zarr for image storage and bioimage.io for AI models) and defining clear pathways to integrate QuPath into analysis pipelines performed in combination with other tools, including Fiji, CellProfiler, and Python. This project aims to strengthen and build upon QuPath’s foundations to ensure that the software meets the needs of its users, even as these needs grow in complexity.