Multi-scale statistical deformation based co-registration of prostate MRI and post-surgical whole mount histopathology.

Autor: Li L; Deptartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA., Shiradkar R; Wallace H Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA., Gottlieb N; Deptartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA., Buzzy C; Deptartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA., Hiremath A; Deptartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA., Viswanathan VS; Wallace H Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA., MacLennan GT; Department of Pathology and Urology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA., Lima DO; Department of Pathology and Urology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA., Gupta K; Department of Pathology and Urology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA., Shen DL; Department of Pathology and Urology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA., Tirumani SH; Department of Radiology, University Hospitals, Cleveland, Ohio, USA., Magi-Galluzzi C; Department of Pathology, University of Alabama at Birmingham, Alabama, USA., Purysko A; Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio, USA.; Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA., Madabhushi A; Wallace H Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA.; Atlanta Veterans Administration Medical Center, Atlanta, Georgia, USA.
Jazyk: angličtina
Zdroj: Medical physics [Med Phys] 2024 Apr; Vol. 51 (4), pp. 2549-2562. Date of Electronic Publication: 2023 Sep 24.
DOI: 10.1002/mp.16753
Abstrakt: Background: Accurate delineations of regions of interest (ROIs) on multi-parametric magnetic resonance imaging (mpMRI) are crucial for development of automated, machine learning-based prostate cancer (PCa) detection and segmentation models. However, manual ROI delineations are labor-intensive and susceptible to inter-reader variability. Histopathology images from radical prostatectomy (RP) represent the "gold standard" in terms of the delineation of disease extents, for example, PCa, prostatitis, and benign prostatic hyperplasia (BPH). Co-registering digitized histopathology images onto pre-operative 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-histopathology co-registration is challenging due to various artifacts and large deformation between in vivo MRI and ex vivo whole-mount histopathology images (WMHs). Furthermore, the artifacts on WMHs, such as tissue loss, may introduce unrealistic deformation during co-registration.
Purpose: This study presents a new registration pipeline, MSERgSDM, a multi-scale feature-based registration (MSERg) with a statistical deformation (SDM) constraint, which aims to improve accuracy of MRI-histopathology co-registration.
Methods: In this study, we collected 85 pairs of MRI and WMHs from 48 patients across three cohorts. Cohort 1 (D 1 ), 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 2 and D 3 ) included 42 patients. Affine and nonrigid registrations were employed to minimize the deformation between ex vivo WMH and ex vivo T2-weighted MRI (T2WI) in D 1 . 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 2 and D 3 , 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-scale representation construction, and (2) SDM construction. For the SDM construction, we collected N = 200 reasonable deformation fields generated using MSERg, verified through visual inspection. Three additional methods, including intensity-based registration, ProsRegNet, and MSERg, were also employed for comparison against MSERgSDM.
Results: Our results suggest that MSERgSDM performed comparably to the ground truth (p > 0.05). Additionally, MSERgSDM (ROI Dice ratio = 0.61, landmark distance = 3.26 mm) exhibited significant improvement over MSERg (ROI Dice ratio = 0.59, landmark distance = 3.69 mm) and ProsRegNet (ROI Dice ratio = 0.56, landmark distance = 4.00 mm) in local alignment.
Conclusions: 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.
(© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)
Databáze: MEDLINE
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