Treffer: Dual-Channel Coupling Approach Enhancing Multi-Stain Pathology Image Matching for Enhancing Cancer Diagnostics.
Title:
Dual-Channel Coupling Approach Enhancing Multi-Stain Pathology Image Matching for Enhancing Cancer Diagnostics.
Authors:
Li X; Institute of Technological Sciences, Wuhan University, Wuhan, China., Ma X; Institute of Technological Sciences, Wuhan University, Wuhan, China., Long M; Institute of Technological Sciences, Wuhan University, Wuhan, China.; Department of Pathology, Peking University Cancer Hospital, Beijing, China., Liu Y; Department of Pathology, Peking University Cancer Hospital, Beijing, China., Wu J; Department of Pathology, Peking University Cancer Hospital, Beijing, China., Xu Y; Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China., Hou J; Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China., Liu S; Institute of Technological Sciences, Wuhan University, Wuhan, China., Wang D; Institute of Technological Sciences, Wuhan University, Wuhan, China., Hu T; Department of Breast Surgery, Peking University People's Hospital, Beijing, China., Mei L; Institute of Technological Sciences, Wuhan University, Wuhan, China.; School of Computer Science, Hubei University of Technology, Wuhan, China., Lei C; Institute of Technological Sciences, Wuhan University, Wuhan, China.; Suzhou Institute of Wuhan University, Suzhou, China.; Shenzhen Institute of Wuhan University, Shenzhen, China.
Source:
Microscopy research and technique [Microsc Res Tech] 2025 Oct; Vol. 88 (10), pp. 2599-2611. Date of Electronic Publication: 2025 May 19.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-Liss Country of Publication: United States NLM ID: 9203012 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0029 (Electronic) Linking ISSN: 1059910X NLM ISO Abbreviation: Microsc Res Tech Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York, NY : Wiley-Liss, c1992-
MeSH Terms:
References:
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Li, J. , J. Dong , S. Huang , X. Li , J. Jiang , and X. Fan . 2024. “Virtual Immunohistochemistry Staining for Histological Images Assisted by Weakly‐Supervised Learning.” Paper presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, June 16–22. https://doi.org/10.1109/CVPR52733.2024.01070.
Li, X. , M. Long , J. Huang , et al. 2023. “An Orientation‐Free Ring Feature Descriptor With Stain‐Variability Normalization for Pathology Image Matching.” Computers in Biology and Medicine 167: 107675. https://doi.org/10.1016/j.compbiomed.2023.107675.
Lin, W. , Y. Hu , R. Zhu , B. Wang , and L. Wang . 2025. “Virtual Staining for Pathology: Challenges, Limitations and Perspectives.” Intelligent Oncology 1: 105–119. https://doi.org/10.1016/j.intonc.2025.03.005.
Liu, S. , B. Zhang , Y. Liu , et al. 2021. “Unpaired Stain Transfer Using Pathology‐Consistent Constrained Generative Adversarial Networks.” IEEE Transactions on Medical Imaging 40, no. 8: 1977–1989. https://doi.org/10.1109/TMI.2021.3069874.
Ma, S. , P. Guo , H. You , P. He , G. Li , and H. Li . 2021. “An Image Matching Optimization Algorithm Based on Pixel Shift Clustering RANSAC.” Information Sciences 562: 452–474. https://doi.org/10.1016/j.ins.2021.03.023.
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Nussinov, R. , B. R. Yavuz , and H. Jang . 2025. “Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge.” Journal of Molecular Biology: 169050. https://doi.org/10.1016/j.jmb.2025.169050.
Parvaiz, A. , M. A. Khalid , R. Zafar , H. Ameer , M. Ali , and M. M. Fraz . 2023. “Vision Transformers in Medical Computer Vision—A Contemplative Retrospection.” Engineering Applications of Artificial Intelligence 122: 106126. https://doi.org/10.1016/j.engappai.2023.106126.
Pautrat, R. , I. Suárez , Y. Yu , M. Pollefeys , and V. Larsson . 2023. “GlueStick: Robust Image Matching by Sticking Points and Lines Together.” Paper presented at 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, October, 1–3. https://doi.org/10.1109/ICCV51070.2023.00890.
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Sun, J. , Z. Shen , Y. Wang , H. Bao , and X. Zhou . 2021. “LoFTR: Detector‐Free Local Feature Matching With Transformers.” Paper presented at 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, June 20–25. https://doi.org/10.1109/CVPR46437.2021.00881.
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Azam, A. B. , F. Wee , J. P. Väyrynen , et al. 2024. “Training Immunophenotyping Deep Learning Models With the Same‐Section Ground Truth Cell Label Derivation Method Improves Virtual Staining Accuracy.” Frontiers in Immunology 15: 1404640. https://doi.org/10.3389/fimmu.2024.1404640.
Bai, B. , X. Yang , Y. Li , Y. Zhang , N. Pillar , and A. Ozcan . 2023. “Deep Learning‐Enabled Virtual Histological Staining of Biological Samples.” Light: Science & Applications 12, no. 1: 57. https://doi.org/10.1038/s41377‐023‐01104‐7.
Beltzung, F. , V. L. Le , I. Molnar , et al. 2025. “Leveraging Deep Learning for Immune Cell Quantification and Prognostic Evaluation in Radiotherapy‐Treated Oropharyngeal Squamous Cell Carcinomas.” Laboratory Investigation 105, no. 4: 104094. https://doi.org/10.1016/j.labinv.2025.104094.
Borovec, J. , J. Kybic , I. Arganda‐Carreras , et al. 2020. “ANHIR: Automatic Non‐Rigid Histological Image Registration Challenge.” IEEE Transactions on Medical Imaging 39, no. 10: 3042–3052. https://doi.org/10.1109/TMI.2020.2986331.
Boveiri, H. R. , R. Khayami , R. Javidan , and A. Mehdizadeh . 2020. “Medical Image Registration Using Deep Neural Networks: A Comprehensive Review.” Computers and Electrical Engineering 87: 106767. https://doi.org/10.1016/j.compeleceng.2020.106767.
Chen, M. , L. Yu , C. Zhi , et al. 2022. “Improved Faster R‐CNN for Fabric Defect Detection Based on Gabor Filter With Genetic Algorithm Optimization.” Computers in Industry 134: 103551. https://doi.org/10.1016/j.compind.2021.103551.
Chen, S. , Y. Shi , Y. Zhang , J. Zhao , C. Zhang , and T. Pei . 2018. “Local Multi‐Feature Hashing Based Fast Matching for Aerial Images.” Information Sciences 442: 173–185. https://doi.org/10.1016/j.ins.2018.02.036.
Chen, Z. , S. Zhao , K. Hu , et al. 2021. “A Hierarchical and Multi‐View Registration of Serial Histopathological Images.” Pattern Recognition Letters 152: 210–217. https://doi.org/10.1016/j.patrec.2021.10.019.
Cheng, X. , L. Zhang , and Y. Zheng . 2018. “Deep Similarity Learning for Multimodal Medical Images.” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6, no. 3: 248–252. https://doi.org/10.1080/21681163.2015.1135299.
Dai, T. , J. Cai , Y. Zhang , S.‐T. Xia , and L. Zhang . 2019. “Second‐Order Attention Network for Single Image Super‐Resolution.” Paper presented at 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 15–20. https://doi.org/10.1109/CVPR.2019.01132.
Dong, Y. , J. Wan , L. Si , Y. Meng , Y. Dong , and S. Liu . 2020. “Deriving Polarimetry Feature Parameters to Characterize Microstructural Features in Histological Sections of Breast Tissues.” IEEE Transactions on Biomedical Engineering 68, no. 3: 881–892. https://doi.org/10.1109/TBME.2020.3019755.
Duanmu, H. , S. Bhattarai , H. Li , et al. 2022. “A Spatial Attention Guided Deep Learning System for Prediction of Pathological Complete Response Using Breast Cancer Histopathology Images.” Bioinformatics 38, no. 19: 4605–4612. https://doi.org/10.1093/bioinformatics/btac558.
Edstedt, J. , G. Bökman , M. Wadenbäck , and M. Felsberg . 2024. “DeDoDe: Detect, Don't Describe—Describe, Don't Detect for Local Feature Matching.” Paper presented at 2024 International Conference on 3D Vision (3DV), Davos, Switzerland, March 18–21. https://doi.org/10.1109/3DV62453.2024.00035.
Ganapathi, K. A. , A. Nicolae , C. Egan , et al. 2024. “Peripheral T‐Cell Lymphomas Expressing CD30 and CD15 Expand the Spectrum of Anaplastic Large Cell Lymphoma, ALK‐Negative.” British Journal of Haematology 204, no. 5: 1862–1871. https://doi.org/10.1111/bjh.19442.
Gao, F. , J. Zhu , H. Jiang , Z. Niu , W. Han , and J. Yu . 2020. “Incremental Focal Loss GANs.” Information Processing & Management 57, no. 3: 102192. https://doi.org/10.1016/j.ipm.2019.102192.
Gatenbee, C. D. , A. M. Baker , S. Prabhakaran , et al. 2023. “Virtual Alignment of Pathology Image Series for Multi‐Gigapixel Whole Slide Images.” Nature Communications 14, no. 1: 4502. https://doi.org/10.1038/s41467‐023‐40218‐9.
Guan, X. , Z. Zhang , Y. Wang , Y. Li , and Y. Zhang . 2025. “Supervised Information Mining From Weakly Paired Images for Breast IHC Virtual Staining.” IEEE Transactions on Medical Imaging 44: 2120–2130. https://doi.org/10.1109/TMI.2024.3525299.
He, Y. , Y. Hu , W. Zhao , et al. 2023. “DarkFeat: Noise‐Robust Feature Detector and Descriptor for Extremely Low‐Light RAW Images.” Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1: 826–834. https://doi.org/10.1609/aaai.v37i1.25161.
Ho, M. Y. , M. S. Wu , and C. M. Wu . 2022. “Ultra‐High‐Resolution Unpaired Stain Transformation via Kernelized Instance Normalization.” In European Conference on Computer Vision, edited by S. Avidan , G. Brostow , M. Cissé , G. M. Farinella , and T. Hassner , 490–505. Springer. https://doi.org/10.1007/978‐3‐031‐19803‐8_29.
Jeenath Shafana, N. , and S. Ayothi . 2025. “Efficient Brain Tumor Detection and Segmentation Using DN‐MRCNN With Enhanced Imaging Technique.” Microscopy Research and Technique 88, no. 7: 2025–2047. https://doi.org/10.1002/jemt.24822.
Jiang, J. , N. B. Larson , N. Prodduturi , T. J. Flotte , and S. N. Hart . 2019. “Robust Hierarchical Density Estimation and Regression for Re‐Stained Histological Whole Slide Image Co‐Registration.” PLoS One 14, no. 7: e0220074. https://doi.org/10.1371/journal.pone.0220074.
Lahiani, A. , I. Klaman , N. Navab , S. Albarqouni , and E. Klaiman . 2020. “Seamless Virtual Whole Slide Image Synthesis and Validation Using Perceptual Embedding Consistency.” IEEE Journal of Biomedical and Health Informatics 25, no. 2: 403–411. https://doi.org/10.1109/JBHI.2020.2975151.
Li, J. , J. Dong , S. Huang , X. Li , J. Jiang , and X. Fan . 2024. “Virtual Immunohistochemistry Staining for Histological Images Assisted by Weakly‐Supervised Learning.” Paper presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, June 16–22. https://doi.org/10.1109/CVPR52733.2024.01070.
Li, X. , M. Long , J. Huang , et al. 2023. “An Orientation‐Free Ring Feature Descriptor With Stain‐Variability Normalization for Pathology Image Matching.” Computers in Biology and Medicine 167: 107675. https://doi.org/10.1016/j.compbiomed.2023.107675.
Lin, W. , Y. Hu , R. Zhu , B. Wang , and L. Wang . 2025. “Virtual Staining for Pathology: Challenges, Limitations and Perspectives.” Intelligent Oncology 1: 105–119. https://doi.org/10.1016/j.intonc.2025.03.005.
Liu, S. , B. Zhang , Y. Liu , et al. 2021. “Unpaired Stain Transfer Using Pathology‐Consistent Constrained Generative Adversarial Networks.” IEEE Transactions on Medical Imaging 40, no. 8: 1977–1989. https://doi.org/10.1109/TMI.2021.3069874.
Ma, S. , P. Guo , H. You , P. He , G. Li , and H. Li . 2021. “An Image Matching Optimization Algorithm Based on Pixel Shift Clustering RANSAC.” Information Sciences 562: 452–474. https://doi.org/10.1016/j.ins.2021.03.023.
Mei, Y. , Y. Fan , Y. Zhou , L. Huang , T. S. Huang , and H. Shi . 2020. “Image Super‐Resolution With Cross‐Scale Non‐Local Attention and Exhaustive Self‐Exemplars Mining.” Paper presented at 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, June 13–19. https://doi.org/10.1109/CVPR42600.2020.00573.
Nussinov, R. , B. R. Yavuz , and H. Jang . 2025. “Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge.” Journal of Molecular Biology: 169050. https://doi.org/10.1016/j.jmb.2025.169050.
Parvaiz, A. , M. A. Khalid , R. Zafar , H. Ameer , M. Ali , and M. M. Fraz . 2023. “Vision Transformers in Medical Computer Vision—A Contemplative Retrospection.” Engineering Applications of Artificial Intelligence 122: 106126. https://doi.org/10.1016/j.engappai.2023.106126.
Pautrat, R. , I. Suárez , Y. Yu , M. Pollefeys , and V. Larsson . 2023. “GlueStick: Robust Image Matching by Sticking Points and Lines Together.” Paper presented at 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, October, 1–3. https://doi.org/10.1109/ICCV51070.2023.00890.
Potje, G. , F. Cadar , A. Araujo , R. Martins , and E. R. Nascimento . 2024. “XFeat: Accelerated Features for Lightweight Image Matching.” Paper presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, June 16–22. https://doi.org/10.1109/CVPR52733.2024.00259.
Roy, M. , F. Wang , G. Teodoro , et al. 2023. “Deep Learning Based Registration of Serial Whole‐Slide Histopathology Images in Different Stains.” Journal of Pathology Informatics 14: 100311. https://doi.org/10.1016/j.jpi.2023.100311.
Seregni, M. , C. Paganelli , P. Summers , M. Bellomi , G. Baroni , and M. Riboldi . 2017. “A Hybrid Image Registration and Matching Framework for Real‐Time Motion Tracking in MRI‐Guided Radiotherapy.” IEEE Transactions on Biomedical Engineering 65, no. 1: 131–139. https://doi.org/10.1109/TBME.2017.2696361.
Sun, J. , Z. Shen , Y. Wang , H. Bao , and X. Zhou . 2021. “LoFTR: Detector‐Free Local Feature Matching With Transformers.” Paper presented at 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, June 20–25. https://doi.org/10.1109/CVPR46437.2021.00881.
Sun, K. , Z. Chen , G. Wang , J. Liu , X. Ye , and Y.‐G. Jiang . 2023. “Bi‐Directional Feature Fusion Generative Adversarial Network for Ultra‐High Resolution Pathological Image Virtual Re‐Staining.” Paper presented at 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, June 17–24. https://doi.org/10.1109/CVPR52729.2023.00380.
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Grant Information:
62475198 National Natural Science Foundation of China; 82202068 National Natural Science Foundation of China; PY202331 Science Foundation of Peking University Cancer Hospital; 2025AFB055 Hubei Provincial Natural Science Foundation of China; 202319 Hubei Province Young Science and Technology Talent Morning Hight Lift Project; BK20221257 Jiangsu Science and Technology Program; JCYJ20220530140601003 Shenzhen Science and Technology Program; JCYJ20230807090207014 Shenzhen Science and Technology Program; Science and Technology Research Project of Education Department of Hubei Province; XJ2023007301 Doctoral Starting up Foundation of Hubei University of Technology
Contributed Indexing:
Keywords: hematoxylin and eosin; images matching; immunohistochemistry; medical image; pathology image
Entry Date(s):
Date Created: 20250519 Date Completed: 20250907 Latest Revision: 20250907
Update Code:
20250907
DOI:
10.1002/jemt.24878
PMID:
40384494
Database:
MEDLINE
Weitere Informationen
Medical image matching is crucial for assisting pathological diagnosis, as it aligns gold standard hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stained pathology images, enabling a comprehensive assessment for identifying cancerous regions. However, manual annotation of multi-stain pathology images incurs high labor costs. To address this challenge, we propose the deep dual-channel coupling (DDC) method for multi-stain pathology image matching. DDC utilizes virtual staining to establish two matching channels, bridging H&E-stained and IHC-stained pathology images while effectively mitigating staining variations. Subsequently, each channel undergoes guided matching using deep descriptor representations of multi-stain pathology images. Finally, a coupling strategy integrates the matching results from both channels, leveraging information from different channels to enhance accuracy and success rates. Experiment results demonstrate that DDC achieves a 93.81% success rate, surpassing the comparison method in estimating the gold standard based on 210 manual annotations. Compared to manual annotation errors, DDC improves accuracy by 45.24%, bringing it closer to the level of clinical manual annotation. Although DDC cannot replace pathologists in fully automated cancer classification, it serves as a limited aid for comprehensive assessments, demonstrating outstanding reliability in distinguishing malignant Hodgkin lymphoma and diagnosing ductal carcinoma in situ of the breast. Therefore, DDC holds significant potential in matching pathology images and supporting clinical pathological diagnostic applications.
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