RAPHIA : A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate
Copyright © 2024 Elsevier Ltd. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:173 |
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| Enthalten in: |
Computers in biology and medicine - 173(2024) vom: 24. Apr., Seite 108318 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Shao, Wei [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Completed 17.04.2024 Date Revised 17.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2024.108318 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Histopathology | |
| 650 | 4 | |a Image registration | |
| 650 | 4 | |a MRI | |
| 650 | 4 | |a Prostate cancer | |
| 650 | 4 | |a Radiology-pathology fusion | |
| 700 | 1 | |a Vesal, Sulaiman |e verfasserin |4 aut | |
| 700 | 1 | |a Soerensen, Simon J C |e verfasserin |4 aut | |
| 700 | 1 | |a Bhattacharya, Indrani |e verfasserin |4 aut | |
| 700 | 1 | |a Golestani, Negar |e verfasserin |4 aut | |
| 700 | 1 | |a Yamashita, Rikiya |e verfasserin |4 aut | |
| 700 | 1 | |a Kunder, Christian A |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Richard E |e verfasserin |4 aut | |
| 700 | 1 | |a Ghanouni, Pejman |e verfasserin |4 aut | |
| 700 | 1 | |a Brooks, James D |e verfasserin |4 aut | |
| 700 | 1 | |a Sonn, Geoffrey A |e verfasserin |4 aut | |
| 700 | 1 | |a Rusu, Mirabela |e verfasserin |4 aut | |
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