Segmentation metric misinterpretations in bioimage analysis - Hirling, Dominik; Tasnadi, Ervin; Caicedo, Juan; Caroprese, Maria V.; Sjögren, Rickard; Aubreville, Marc; Koos, Krisztian; Horvath, Peter (Nature methods, 2024)
Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled. This study shows the importance of proper metrics for comparing algorithms for bioimage segmentation and object detection by exploring the impact of metrics on the relative performance of algorithms in three image analysis competitions.