registration.

Previous work in the lung [@rusu_framework_2015; @rusu_co-registration_2017], breast [@rusu_rad-path_2019] or prostate [@losnegard_intensity-based_2018; @rusu_framework_2019], has relied on approaches that reconstruct the sequential histopathology slices and created a 3D volume representing the histopathology specimen prior to sectioning, which facilitates the spatial registration with the 3D volumetric MRI and alleviates the need for slice correspondences. However, these methods are prone to overfitting the histopathology reconstruction due to the large number of degrees of freedom and may suffer from partial volume effects due to the missing data associated with thick MRI slices and the histopathology slice spacing.

Our approach makes the following contributions: 1) Our registration methodology combines a 3D reconstruction of the histopathology specimen with 2D affine and deformable registration of corresponding histopathology and MRI slices and was optimized for an accurate alignment, 2) Our approach was tested in a digital phantom where the ground truth is known as well as in the largest cohort considered to date in a radiology-pathology registration study, and 3) To the best of our knowledge, we are the first to release the source code for the registration of histopathology and radiology images in the prostate, [which is essential to test the reproducibility and robustness of the 