Lazy Luna: Extendible software for multilevel reader comparison in cardiovascular magnetic resonance imaging

Background and objectives: Cardiovascular Magnetic Resonance (CMR) imaging is a growing field with increasing diagnostic utility in clinical routine. Quantitative diagnostic parameters are typically calculated based on contours or points provided by readers, e.g. natural intelligences (NI) such as clinicians or re-searchers, and artificial intelligences (AI). As clinical applications multiply, evaluating the precision and reproducibility of quantitative parameters becomes increasingly important. Although segmentation chal-lenges for AIs and guidelines for clinicians provide quality assessments and regulation, the methods ought to be combined and streamlined for clinical applications. The goal of the developed software, Lazy Luna (LL), is to offer a flexible evaluation tool that is readily extendible to new sequences and scientific endeavours. Methods: An interface was designed for LL, which allows for comparing annotated CMR images. Ge-ometric objects ensure precise calculations of metric values and clinical results regardless of whether annotations originate from AIs or NIs. A graphical user interface (GUI) is provided to make the software available to non-programmers. The GUI allows for an interactive inspection of image datasets as well as implementing tracing procedures, which follow statistical reader differences in clinical results to their origins in individual image contours. The backend software builds on a set of meta-classes, which can be extended to new imaging sequences and clinical parameters. Following an agile development procedure with clinical feedback allows for a quick implementation of new classes, figures and tables for evaluation. Results: Two application cases present LL's extendibility to clinical evaluation and AI development con-texts. The first concerns T1 parametric mapping images segmented by two expert readers. Quantitative result differences are traced to reveal typical segmentation dissimilarities from which these differences originate. The meta-classes are extended to this new application scenario. The second applies to the open source Late Gadolinium Enhancement (LGE) quantification challenge for AI developers Emidec, which il-lustrates LL's usability as open source software. Conclusion: The presented software Lazy Luna allows for an automated multilevel comparison of readers as well as identifying qualitative reasons for statistical reader differences. The open source software LL can be extended to new application cases in the future. & COPY; 2023 Published by Elsevier B.V.

Automated summary

This study developed software called Lazy Luna for the quantitative evaluation of cardiovascular magnetic resonance imaging. The software allows for comparing annotated images from artificial intelligence and clinicians, ensuring accurate metric calculations. It provides a graphical user interface for non-programmers and enables the tracing of differences in segmentation results among readers to identify the origins of these differences. The software can be extended to new application cases.