  Cross-slide image analysis provides additional information by analysing the
expression of different biomarkers as compared to a single slide analysis.
These biomarker stained slides are analysed side by side, revealing unknown
relations between them. During the slide preparation, a tissue section may be
placed at an arbitrary orientation as compared to other sections of the same
tissue block. The problem is compounded by the fact that tissue contents are
likely to change from one section to the next and there may be unique artefacts
on some of the slides. This makes registration of each section to a reference
section of the same tissue block an important pre-requisite task before any
cross-slide analysis. We propose a deep feature based registration (DFBR)
method which utilises data-driven features to estimate the rigid
transformation. We adopted a multi-stage strategy for improving the quality of
registration. We also developed a visualisation tool to view registered pairs
of WSIs at different magnifications. With the help of this tool, one can apply
a transformation on the fly without the need to generate transformed source WSI
in a pyramidal form. We compared the performance of data-driven features with
that of hand-crafted features on the COMET dataset. Our approach can align the
images with low registration errors. Generally, the success of non-rigid
registration is dependent on the quality of rigid registration. To evaluate the
efficacy of the DFBR method, the first two steps of the ANHIR winner's
framework are replaced with our DFBR to register challenge provided image
pairs. The modified framework produces comparable results to that of challenge
winning team.
