Opta ultrasound imaging made easier with open source analysis software
OPTARI: An open-source software framework for clinical translation of optoacoustic imaging
Software EngineeringComputer Vision and Pattern Recognition
Summary
Optoacoustic and ultrasound imaging can help study many diseases, but the software to analyze these images is often hard to use for people who don't code. The authors developed OPTARI, a free program with a user-friendly interface that works with many data types and helps users mark areas, separate tissue types, and analyze signals faster. Their software matches expert tissue labeling and speeds processing from minutes to seconds per scan. OPTARI helps connect technical tools with medical research in a reliable and repeatable way.
What this means in practice
- •For clinical imaging teams: Analyze large optoacoustic-ultrasound clinical studies efficiently with a graphical tool that supports vendor data and automates tissue segmentation.
- •For medical device developers: Integrate open-source software to enable standardized and reproducible processing of optoacoustic imaging data for new clinical imaging devices.
Authors
Moritz Schillinger, Maximilian Bader, Adrian Buehler, Felix Wachter, Ferdinand Knieling, Vasilis Ntziachristos, Katharina Breininger
Abstract
Combined optoacoustic (OptA) and ultrasound imaging is a promising modality across a wide range of pathologies. Its use in clinical research, however, is limited by a lack of analysis software accessible to users without programming expertise. Existing tools are either script-based engineering frameworks or proprietary vendor applications. We present OPTARI, an open-source graphical software application to facilitate large-scale clinical optoacoustic-ultrasound (OPUS) studies. OPTARI builds on the napari image viewer and the PATATO toolkit for OptA data processing. It supports native vendor and open HDF5 data formats, aligns with the IPASC standard, and is distributed as a standalone executable. Features include region-of-interest annotation, reconstruction, chromophore unmixing, deep-learning-based tissue segmentation, and temporal and spectral analysis modules. OPTARI offers a combination of features for clinical study analysis that none of the compared tools provide. The automated tissue segmentation produced results comparable to expert labeling for muscle tissue, and batch processing reduced analysis time from minutes to under one second per scan. The tool is used in ongoing clinical OPUS studies. OPTARI connects engineering frameworks with medical research and provides an open-source basis for reproducible and standardized analysis in clinical OptA imaging studies.