A statistical deformation model (SDM) based regularizer for non-rigid image registration: application to registration of multimodal prostate MRI and histology

Prabu, Srivathsan Babu; Tóth, Róbert; Madabhushi, Anant

doi:10.1117/12.2008707

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…The reconstructed pseudo‐WMHs contain more artifacts than actual WMHs, such as tissue folding and tissue loss, which require additional regularization during registration. Previous studies demonstrated that a collection of reasonable deformations can be parameterized as a statistical deformation model (SDM) to reflect the deformation distribution and infer anatomical changes between the target and reference images 23–25 . By incorporating a prior knowledge of deformations between in vivo T2WI and ex vivo WMHs, an SDM can provide a representation of the deformation distribution and estimate the likelihood of a new deformation being reasonable.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Shiradkar,

Gottlieb

et al. 2023

Medical Physics

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BackgroundAccurate 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.PurposeThis 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.MethodsIn this study, we collected 85 pairs of MRI and WMHs from 48 patients across three cohorts. Cohort 1 (D1), 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 (D2 and D3) 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 D1. 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 D2 and D3, 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.ResultsOur 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.ConclusionsThis 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.

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Section: Introductionmentioning

confidence: 99%

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

Li,

Shiradkar,

Gottlieb

et al. 2023

Medical Physics

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A statistical deformation model based regularizer for registration of histology and MRI

Koundinyan

Tóth

Madabhushi

et al. 2014

2014 40th Annual Northeast Bioengineering Conference (NEBEC)

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Free form deformation (FFD) is a popular algorithm for non-linear image registration because of its ability to accurately recover deformations. However, due to the unconstrained elastic registration, FFD may introduce unrealistic deformations, especially when differences between template and target image are large, thereby necessitating a regularizer to constrain the registration to a physically meaning transformation. Prior knowledge in the form of a Statistical Deformation Model (SDM) in a registration scheme has been shown to function as an effective regularizer. With a similar underlying premise, in this paper, we present a novel regularizer for FFD that leverages knowledge of known, valid deformations to train a statistical deformation model (SDM). At each iteration of the FFD registration, the SDM is utilized to calculate the likelihood of a given deformation occurring and appropriately influence the similarity metric to limit the registration to only realistic deformations. Qualitative evaluation of the SDM regularizer was performed for registration of ex vivo pseudo-whole mount histology (WMH) and in vivo prostate MRI to quantify multiprotocol MRI signatures of prostate cancer (CaP). Knowledge from a training set of 6 deformation fields gathered from a single patient study is used to deliver accurate overlays of prostate histology and MRI.

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A Novel Non-rigid Registration Method Based on Nonparametric Statistical Deformation Model for Medical Image Analysis

Cui

Mahmoodi

Conway

et al. 2017

2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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A statistical deformation model (SDM) based regularizer for non-rigid image registration: application to registration of multimodal prostate MRI and histology

Cited by 4 publications

References 22 publications

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

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

A statistical deformation model based regularizer for registration of histology and MRI

A Novel Non-rigid Registration Method Based on Nonparametric Statistical Deformation Model for Medical Image Analysis

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