Metrics Reloaded - A new recommendation framework for biomedical image analysis validation

Annika Reinke, Lena Maier-Hein, Evangelia Christodoulou, Ben Glocker, Patrick Scholz, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael Alexander Riegler, Manuel Wiesenfarth, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, Ali Emre Kavur, Tim Rädsch, Minu D. Tizabi, Laura Acion, Michela Antonelli, Tal Arbel et al. (48 additional authors not shown)

Keywords: Validation, Metrics, Good Scientific Practice, Classification, Semantic Segmentation, Object Detection, Instance Segmentation, Medical Imaging

Abstract: Meaningful performance assessment of biomedical image analysis algorithms depends on objective and appropriate performance metrics. There are major shortcomings in the current state of the art. Yet, so far limited attention has been paid to practical pitfalls associated when using particular metrics for image analysis tasks. Therefore, a number of international initiatives have collaborated to offer researchers with guidance and tools for selecting performance metrics in a problem-aware manner. In our proposed framework, the characteristics of the given biomedical problem are first captured in a problem fingerprint, which identifies properties related to domain interests, the target structure(s), the input datasets, and algorithm output. A problem category-specific mapping is applied in the second step to match fingerprints to metrics that reflect domain requirements. Based on input from experts from more than 60 institutions worldwide, we believe our metric recommendation framework to be useful to the MIDL community and to enhance the quality of biomedical image analysis algorithm validation.

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Paper Type: recently published or submitted journal contributions

Primary Subject Area: Segmentation

Secondary Subject Area: Validation Study

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