fiducials, Ex vivo MRI & NA & 1.1\
Kalavagunta 2014 [@kalavagunta_registration_2015] & 35 & Local affine registration & Internal landmarks, 3D Printed Molds & 0.99 & 1.54$\pm$0.64\
Reynolds 2015 [@reynolds_development_2015] &6& 2D TPS registration + deformable registration & Control Points, ex vivo MRI, sectioning box & 0.93 & 3.3\
Li 2017 [@li_co-registration_2017] & 19 & Multi-Scale Representation + deformable registration & - & 0.96$\pm$0.01 &2.96$\pm$0.76\
Losnegard 2018 [@losnegard_intensity-based_2018] & 12 & 3D histopathology reconstruction, 3D affine and deformable registration & - & 0.94 & 5.4\
Wu 2019 [@wu_system_2019] & 17 & 2D Rigid, TPS Registration (automatic landmarks) & ex vivo MRI, 3D printed molds & 0.87$\pm$0.04&2.0$\pm$0.5\
Rusu 2019 [@rusu_framework_2019] & 15 & 3D histopathology reconstruction, 2D Affine+Deformable & 3D printed Molds &0.94$\pm0.02$& 1.11$\pm$0.34\

Although numerous approaches for the radiology-pathology registration in the prostate have been introduced (see section “Prior Work“), these approaches have not been widely adopted and have not been carefully tested by scientists outside the developer teams. Recent publications using histopathology images as reference to improve MRI and automatically detect cancer [@penzias_identifying_2018; @hurrell_optimized_2018; @sumathipala_prostate_2018; @cao_joint_2019; @reynolds_voxel-wise_2019] still use manual approaches to align the histopathology to MRI images, which are known to be labor-intensive and subjective. The reduced adoption 