학술논문
RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate.
Document Type
Academic Journal
Author
Shao W; Department of Radiology, Stanford University, Stanford, CA, 94305, United States; Department of Medicine, University of Florida, Gainesville, FL, 32610, United States. Electronic address: weishao@ufl.edu.; Vesal S; Department of Urology, Stanford University, Stanford, CA, 94305, United States.; Soerensen SJC; Department of Urology, Stanford University, Stanford, CA, 94305, United States; Department of Epidemiology and Population Health, Stanford University, Stanford, CA, 94305, United States.; Bhattacharya I; Department of Radiology, Stanford University, Stanford, CA, 94305, United States.; Golestani N; Department of Radiology, Stanford University, Stanford, CA, 94305, United States.; Yamashita R; Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, United States.; Kunder CA; Department of Pathology, Stanford University, Stanford, CA, 94305, United States.; Fan RE; Department of Urology, Stanford University, Stanford, CA, 94305, United States.; Ghanouni P; Department of Radiology, Stanford University, Stanford, CA, 94305, United States.; Brooks JD; Department of Urology, Stanford University, Stanford, CA, 94305, United States.; Sonn GA; Department of Radiology, Stanford University, Stanford, CA, 94305, United States; Department of Urology, Stanford University, Stanford, CA, 94305, United States.; Rusu M; Department of Radiology, Stanford University, Stanford, CA, 94305, United States. Electronic address: mirabela.rusu@stanford.edu.
Source
Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE
Subject
Language
English
Abstract
Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignment (RAPHIA), an end-to-end pipeline for efficient and accurate registration of MRI and histopathology images. RAPHIA automates several time-consuming manual steps in existing approaches including prostate segmentation, estimation of the rotation angle and horizontal flipping in histopathology images, and estimation of MRI-histopathology slice correspondences. By utilizing deep learning registration networks, RAPHIA substantially reduces computational time. Furthermore, RAPHIA obviates the need for a multimodal image similarity metric by transferring histopathology image representations to MRI image representations and vice versa. With the assistance of RAPHIA, novice users achieved expert-level performance, and their mean error in estimating histopathology rotation angle was reduced by 51% (12 degrees vs 8 degrees), their mean accuracy of estimating histopathology flipping was increased by 5% (95.3% vs 100%), and their mean error in estimating MRI-histopathology slice correspondences was reduced by 45% (1.12 slices vs 0.62 slices). When compared to a recent conventional registration approach and a deep learning registration approach, RAPHIA achieved better mapping of histopathology cancer labels, with an improved mean Dice coefficient of cancer regions outlined on MRI and the deformed histopathology (0.44 vs 0.48 vs 0.50), and a reduced mean per-case processing time (51 vs 11 vs 4.5 min). The improved performance by RAPHIA allows efficient processing of large datasets for the development of machine learning models for prostate cancer detection on MRI. Our code is publicly available at: https://github.com/pimed/RAPHIA.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mirabela Rusu is a paid consultant for Roche, the conflict is unrelated to this research. Also, Mirabela Rusu has research grants from Phillips Healthcare. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mirabela Rusu is a paid consultant for Roche, the conflict is unrelated to this research. Also, Mirabela Rusu has research grants from Phillips Healthcare. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)