Multi‐scale statistical deformation based co‐registration of prostate MRI and post‐surgical whole mount histopathology
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 24, 2023
- Source ID
- 10.1002/mp.16753
Entities
People
- Amogh Hiremath
- Anant Madabhushi
- Andrei Purysko
- Christina Buzzy
- Cristina Magi‐galluzzi
- Daniel Lee Shen
- Danly Omil Lima
- Gregory T. Maclennan
- Karishma Gupta
- Lin Li
- Noah Gottlieb
- Rakesh Shiradkar
- Sree Harsha Tirumani
- Vidya Sankar Viswanathan
Organizations
- Case Western Reserve University
- Cleveland Clinic
- Emory University
- National Cancer Institute
- National Center for Research Resources
- National Institute of Biomedical Imaging and Bioengineering
- UH Cleveland Medical Center
- University of Alabama at Birmingham