Framework for the co-registration of MRI and histology images in prostate cancer patients with radical prostatectomy

Abstract

Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis. It can spare men with a normal exam from undergoing invasive biopsy while making biopsies more accurate in men with lesions suspicious for cancer. Yet, the subtle differences between cancer and confounding conditions, render the interpretation of MRI challenging. The tissue collected from patients that undergo pre-surgical MRI and radical prostatectomy provides a unique opportunity to correlate histopathology images of the entire prostate with MRI in order to accurately map the extent of prostate cancer onto MRI. Such mapping will help improve existing MRI interpretation schemes, e.g. PIRADS, and will facilitate the development of quantitative image analysis methods to assess the imaging characteristics of prostate cancer on MRI. Here, we introduce the RAPSODI (RAdiology Pathology Spatial Open-Source multi-Dimensional Integration) framework for the registration of radiology and pathology images. RAPSODI relies on a three-step procedure that first reconstructs in three dimensions (3D) the resected tissue using the serial whole-mount histopathology slices, then registers corresponding histopathology and MRI slices, and finally maps the cancer outlines from the histopathology slices onto MRI. We tested RAPSODI in a phantom study where we simulated various conditions, e.g., tissue specimen rotation upon 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 ±15 • 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±0.01 for the prostate, prostate boundary Hausdorff distance of 1.71±0.48 mm, a urethra deviation of 2.91±1.25 mm, and a landmark deviation of 2.88±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 opensource 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.

Registration of presurgical MRI and histopathology images from radical prostatectomy via RAPSODI

“…RAPSODI first reconstructs the histopathology volume, followed by a slice‐to‐slice alignment between the corresponding histopathology and T2w images. As shown in prior studies in the prostate 27,28 and other organs, 29–31 the reconstruction ensures the consistent stacking of the histopathology slices relative to each other, independent of the MRI, which results in a better initialization in the registration with the MR images. Unlike prior studies 27–31 that performed 3D registrations which are prone to overfitting due to a large number of degrees of freedom.…”

“…As shown in prior studies in the prostate 27,28 and other organs, 29–31 the reconstruction ensures the consistent stacking of the histopathology slices relative to each other, independent of the MRI, which results in a better initialization in the registration with the MR images. Unlike prior studies 27–31 that performed 3D registrations which are prone to overfitting due to a large number of degrees of freedom. RAPSODI performs the registration between MRI and corresponding histopathology images after reconstruction thus combining the benefits of the 2D registration to reduce the degrees of freedom with the 3D reconstruction to maintain 3D consistency.…”

“…We first evaluated RAPSODI in a digital phantom and showed that our framework can recover the rotation of the histopathology slices resulting from glass slide mounting when these angles are within 15 ∘ from the correct solution and with tissue shrinkage up to 10%. Correcting for large rotation angles can be achieved prior to applying RAPSODI either using automated approaches, for example, by aligning the major axis of the data, 28 or having an expert user indicating an angle, as was done in our study. The tissue shrinks during fixation with a factor that is outside our control.…”

ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate

“…From this segmentation, a 3D volume will be created, which is then imported into a modeling software to create a personalized mold such that the orientation of the prostate specimen is aligned with the original MRI to guide the gross sectioning of the ex vivo prostate to have exact slice correspondences with the MRI Priester et al (2014) . Several approaches Losnegård et al (2018) ; Rusu et al (2019) ; Wu et al (2019) rely on patient-specific 3D printed molds to establish better histopathology and MRI slice correspondences in order to improve the registration of MRI and histopathology images. While some approaches work directly with MR and histopathology images alone, others require additional steps including a separate ex vivo MRI of the prostate Wu et al (2019) , fiducial markers Ward et al (2012) , or advanced image similarity metrics Chappelow et al (2011) ; Li et al (2017) .…”

“…While some approaches work directly with MR and histopathology images alone, others require additional steps including a separate ex vivo MRI of the prostate Wu et al (2019) , fiducial markers Ward et al (2012) , or advanced image similarity metrics Chappelow et al (2011) ; Li et al (2017) . Several pipelines have been developed for direct integration of MR and histopathology images by 3D histopathology volume reconstruction Losnegård et al (2018) ; Rusu et al (2019) ; Samavati et al (2011) ; Stille et al (2013) , but most are time-consuming, computationally expensive, and can suffer from partial volume effects and artifacts due to large spacing between images.…”