employed, even in recent publications [@penzias_identifying_2018; @hurrell_optimized_2018; @sumathipala_prostate_2018; @cao_joint_2019; @reynolds_voxel-wise_2019]. Some manual or semi-automatic approaches utilize landmark-based registration approaches, either alone [@penzias_identifying_2018; @hurrell_optimized_2018; @reynolds_voxel-wise_2019] or in combination with automated registration steps [@reynolds_development_2015; @hurrell_optimized_2018]. These approaches are labor-intensive and require the human operator to possess expertise in both MRI and histopathology, and necessitate identification of corresponding landmarks on both modalities. Other such approaches [@turkbey_multiparametric_2011; @sumathipala_prostate_2018] employ cognitive alignment in which a radiologist with the help of a pathologist directly outlines the cancer region on MRI considering the histopathology images as reference. Such methods are tedious to apply and may be prone to underestimating the dimensions of the lesion [@priester_magnetic_2017] while MRI invisible lesions are hard if not impossible to outline and thereby they are often omitted from follow-up analysis. A few approaches use interactive image transformations [@costa_improved_2017], in which a user indicate scaling, rotations and translations to be applied to the images. Such approaches are also tedious to utilize and require extensive knowledge in both radiology and pathology of the prostate.

The automated registration of histopathology images with pre-surgical prostate MRI has been performed in proof-of-concept studies, which usually only include a small number of subjects, often &lt; 20 ( \[tab:prior\_work\]). 