Framing image registration as a landmark detection problem for better
  representation of clinical relevance
Diana Waldmannstetter Benedikt Wiestler Julian Schwarting Ivan Ezhov Marie Metz Spyridon Bakas Bhakti Baheti Satrajit Chakrabarty Jan S. Kirschke Rolf A. Heckemann Marie Piraud Florian Kofler Bjoern H. Menze
Nowadays, registration methods are typically evaluated based on
sub-resolution tracking error differences. In an effort to reinfuse this
evaluation process with clinical relevance, we propose to reframe image
registration as a landmark detection problem. Ideally, landmark-specific
detection thresholds are derived from an inter-rater analysis. To approximate
this costly process, we propose to compute hit rate curves based on the
distribution of errors of a sub-sample inter-rater analysis. Therefore, we
suggest deriving thresholds from the error distribution using the formula:
median + delta * median absolute deviation. The method promises differentiation
of previously indistinguishable registration algorithms and further enables
assessing the clinical significance in algorithm development.