  Registration of histological serial sections is a challenging task. Serial
sections exhibit distortions and damage from sectioning. Missing information on
how the tissue looked before cutting makes a realistic validation of 2D
registrations extremely difficult.
  This work proposes methods for ground-truth-based evaluation of
registrations. Firstly, we present a methodology to generate test data for
registrations. We distort an innately registered image stack in the manner
similar to the cutting distortion of serial sections. Test cases are generated
from existing 3D data sets, thus the ground truth is known. Secondly, our test
case generation premises evaluation of the registrations with known ground
truths. Our methodology for such an evaluation technique distinguishes this
work from other approaches. Both under- and over-registration become evident in
our evaluations. We also survey existing validation efforts.
  We present a full-series evaluation across six different registration methods
applied to our distorted 3D data sets of animal lungs. Our distorted and ground
truth data sets are made publicly available.
