  While the importance of automatic image analysis is continuously increasing,
recent meta-research revealed major flaws with respect to algorithm validation.
Performance metrics are particularly key for meaningful, objective, and
transparent performance assessment and validation of the used automatic
algorithms, but relatively little attention has been given to the practical
pitfalls when using specific metrics for a given image analysis task. These are
typically related to (1) the disregard of inherent metric properties, such as
the behaviour in the presence of class imbalance or small target structures,
(2) the disregard of inherent data set properties, such as the non-independence
of the test cases, and (3) the disregard of the actual biomedical domain
interest that the metrics should reflect. This living dynamically document has
the purpose to illustrate important limitations of performance metrics commonly
applied in the field of image analysis. In this context, it focuses on
biomedical image analysis problems that can be phrased as image-level
classification, semantic segmentation, instance segmentation, or object
detection task. The current version is based on a Delphi process on metrics
conducted by an international consortium of image analysis experts from more
than 60 institutions worldwide.
