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However, manual ROI delineations are labor\u2010intensive and susceptible to inter\u2010reader variability. Histopathology images from radical prostatectomy (RP) represent the \u201cgold standard\u201d in terms of the delineation of disease extents, for example, PCa, prostatitis, and benign prostatic hyperplasia (BPH). Co\u2010registering digitized histopathology images onto pre\u2010operative mpMRI enables automated mapping of the ground truth disease extents onto mpMRI, thus enabling the development of machine learning tools for PCa detection and risk stratification. Still, MRI\u2010histopathology co\u2010registration is challenging due to various artifacts and large deformation between in vivo MRI and ex vivo whole\u2010mount histopathology images (WMHs). Furthermore, the artifacts on WMHs, such as tissue loss, may introduce unrealistic deformation during co\u2010registration.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study presents a new registration pipeline, MSERgSDM, a multi\u2010scale feature\u2010based registration (MSERg) with a statistical deformation (SDM) constraint, which aims to improve accuracy of MRI\u2010histopathology co\u2010registration.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we collected 85 pairs of MRI and WMHs from 48 patients across three cohorts. Cohort 1 (D<jats:sub>1<\/jats:sub>), comprised of a unique set of 3D printed mold data from six patients, facilitated the generation of ground truth deformations between ex vivo WMHs and in vivo MRI. The other two clinically acquired cohorts (D<jats:sub>2<\/jats:sub> and D<jats:sub>3<\/jats:sub>) included 42 patients. Affine and nonrigid registrations were employed to minimize the deformation between ex vivo WMH and ex vivo T2\u2010weighted MRI (T2WI) in D<jats:sub>1<\/jats:sub>. Subsequently, ground truth deformation between in vivo T2WI and ex vivo WMH was approximated as the deformation between in vivo T2WI and ex vivo T2WI. In D<jats:sub>2<\/jats:sub> and D<jats:sub>3<\/jats:sub>, the prostate anatomical annotations, for example, tumor and urethra, were made by a pathologist and a radiologist in collaboration. These annotations included ROI boundary contours and landmark points. Before applying the registration, manual corrections were made for flipping and rotation of WMHs. MSERgSDM comprises two main components: (1) multi\u2010scale representation construction, and (2) SDM construction. For the SDM construction, we collected <jats:italic>N<\/jats:italic>\u00a0=\u00a0200 reasonable deformation fields generated using MSERg, verified through visual inspection. Three additional methods, including intensity\u2010based registration, ProsRegNet, and MSERg, were also employed for comparison against MSERgSDM.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our results suggest that MSERgSDM performed comparably to the ground truth (<jats:italic>p<\/jats:italic>\u00a0&gt;\u00a00.05). Additionally, MSERgSDM (ROI Dice ratio\u00a0=\u00a00.61, landmark distance\u00a0=\u00a03.26\u00a0mm) exhibited significant improvement over MSERg (ROI Dice ratio\u00a0=\u00a00.59, landmark distance\u00a0=\u00a03.69\u00a0mm) and ProsRegNet (ROI Dice ratio\u00a0=\u00a00.56, landmark distance\u00a0=\u00a04.00\u00a0mm) in local alignment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study presents a novel registration method, MSERgSDM, for mapping ex vivo WMH onto in vivo prostate MRI. Our preliminary results demonstrate that MSERgSDM can serve as a valuable tool to map ground truth disease annotations from histopathology images onto MRI, thereby assisting in the development of machine learning models for PCa detection on MRI.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16753","type":"journal-article","created":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T20:25:40Z","timestamp":1695587140000},"page":"2549-2562","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi\u2010scale statistical deformation based co\u2010registration of prostate MRI and post\u2010surgical whole mount histopathology"],"prefix":"10.1002","volume":"51","author":[{"given":"Lin","family":"Li","sequence":"first","affiliation":[{"name":"Deptartment of Biomedical Engineering Case Western Reserve University  Cleveland Ohio 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