mounting on glass slides, tissue shrinkage during fixation, or imperfect slice-to-slice correspondences between histopathology and MRI images. Our experiments showed that RAPSODI can reliably correct for rotations within $\pm15^{\circ}$ and shrinkage up to 10%. We also evaluated RAPSODI in 89 patients from two institutions that underwent radical prostatectomy, yielding 543 histopathology slices that were registered to corresponding T2 weighted MRI slices. We found a Dice similarity coefficient of 0.98$ \pm $0.01 for the prostate, prostate boundary Hausdorff distance of 1.71$ \pm $0.48 mm, a urethra deviation of 2.91$ \pm $1.25 mm, and a landmark deviation of 2.88$ \pm $0.70 mm between registered histopathology images and MRI. Our robust framework successfully mapped the extent of disease from histopathology slices onto MRI and created ground truth labels for characterizing prostate cancer on MRI. Our open-source RAPSODI platform is available as a 3D Slicer plugin or as a stand-alone program and can be downloaded from <https://github.com/pimed/Slicer-RadPathFusion>.'
author:
- 'Mirabela Rusu[^1]'
- 'Christian A. Kunder'
- 'Nikola C. Teslovich'
- 'Jeffrey B. Wang'
- 'Rewa R. Sood'
- Wei Shao
- 'Leo C. Chen'
- Robert West
- Richard Fan
- Pejman Ghanouni
- 'James D. Brooks'
- 'Geoffrey A. Sonn'
bibliography:
- 'draft.bib'
