{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T21:07:19Z","timestamp":1789074439549,"version":"build-2803163510"},"reference-count":58,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Virtual histological staining offers a rapid, cost-effective alternative to physical reprocessing but faces challenges related to spatial misalignment and staining heterogeneity between Hematoxylin and Eosin (H&amp;E) and Masson\u2019s Trichrome (MT) domains. This study develops a robust framework for H&amp;E-to-MT virtual staining to enable accurate fibrosis assessment without additional tissue consumption. Methods: We propose a transformer-based generative adversarial network (TbGAN) supported by a multi-stage alignment pipeline (SIFT (scale-invariant feature transform) coarse alignment, ORB\/homography patch registration, and B-spline free-form deformation) and a weighted fusion mechanism combining four configuration outputs (O\/10\/3, O\/3\/10, R\/10\/3, and R\/3\/10). The framework was validated on 27 whole-slide images (&gt;100,000 aligned patches) through 24 independent experiments. Results: The fused approach achieved state-of-the-art performance: MI = 0.9815 \u00b1 0.0934, SSIM = 0.7474 \u00b1 0.0597, NCC = 0.9320 \u00b1 0.0220, and CS = 0.9946 \u00b1 0.0014. Statistical analysis confirmed enhanced stability through narrower interquartile ranges, fewer outliers, and tighter 95% confidence intervals compared to individual configurations. Qualitative assessment demonstrated preserved collagen morphology critical for fibrosis staging. Conclusions: Our framework provides a reliable, IRB-compliant solution for virtual MT staining that maintains high structural fidelity suitable for diagnostic support. It enables resource-efficient fibrosis quantification and supports integration into clinical digital pathology workflows without patient-specific recalibration.<\/jats:p>","DOI":"10.3390\/diagnostics16050764","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T08:51:33Z","timestamp":1772614293000},"page":"764","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["From Hematoxylin and Eosin to Masson\u2019s Trichrome: A Comprehensive Framework for Virtual Stain Transformation in Chronic Liver Disease Diagnosis"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0686-4411","authenticated-orcid":false,"given":"Hossam Magdy","family":"Balaha","sequence":"first","affiliation":[{"name":"Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7556-7173","authenticated-orcid":false,"given":"Khadiga M.","family":"Ali","sequence":"additional","affiliation":[{"name":"Pathology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Aboudessouki","sequence":"additional","affiliation":[{"name":"Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed T.","family":"Azam","sequence":"additional","affiliation":[{"name":"Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guruprasad A.","family":"Giridharan","sequence":"additional","affiliation":[{"name":"Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0604-8403","authenticated-orcid":false,"given":"Dibson","family":"Gondim","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7264-1323","authenticated-orcid":false,"given":"Ayman","family":"El-Baz","sequence":"additional","affiliation":[{"name":"Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mezei, T., Kolcsar, M., Joo, A., and Gurzu, S. (2024). Image analysis in histopathology and cytopathology: From early days to current perspectives. J. Imaging, 10.","DOI":"10.3390\/jimaging10100252"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103776","DOI":"10.1016\/j.critrevonc.2022.103776","article-title":"The evolving landscape of anatomic pathology","volume":"178","author":"Pisapia","year":"2022","journal-title":"Crit. Rev. Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15539","DOI":"10.3748\/wjg.v20.i42.15539","article-title":"Histopathology of nonalcoholic fatty liver disease\/nonalcoholic steatohepatitis","volume":"20","author":"Takahashi","year":"2014","journal-title":"World J. Gastroenterol. WJG"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Heyens, L.J., Busschots, D., Koek, G.H., Robaeys, G., and Francque, S. (2021). Liver fibrosis in non-alcoholic fatty liver disease: From liver biopsy to non-invasive biomarkers in diagnosis and treatment. Front. Med., 8.","DOI":"10.3389\/fmed.2021.615978"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1172\/JCI93557","article-title":"Extracellular matrix as a driver of progressive fibrosis","volume":"128","author":"Herrera","year":"2018","journal-title":"J. Clin. Investig."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.matbio.2018.01.013","article-title":"Extracellular matrix remodeling and cardiac fibrosis","volume":"68","author":"Li","year":"2018","journal-title":"Matrix Biol."},{"key":"ref_7","first-page":"128","article-title":"A Review of Basic Histopathological Staining Techniques","volume":"6","author":"Okobi","year":"2023","journal-title":"Int. Res. J. Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","article-title":"Histopathological image analysis: A review","volume":"2","author":"Gurcan","year":"2009","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.cmpb.2011.12.007","article-title":"Histology image analysis for carcinoma detection and grading","volume":"107","author":"He","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_10","unstructured":"Lindberg, M.R. (2022). Diagnostic Pathology: Normal Histology, Elsevier Health Sciences."},{"key":"ref_11","unstructured":"Stonard, C.M. (2011). Cellular pathology. Biomedical Sciences: Essential Laboratory Medicine, John Wiley & Sons."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Arjmand, A., Tsipouras, M.G., Tzallas, A.T., Forlano, R., Manousou, P., and Giannakeas, N. (2020). Quantification of liver fibrosis\u2014A comparative study. Appl. Sci., 10.","DOI":"10.3390\/app10020447"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/bs.mcb.2017.08.005","article-title":"Methods for the visualization and analysis of extracellular matrix protein structure and degradation","volume":"Volume 143","author":"Leonard","year":"2018","journal-title":"Methods in Cell Biology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1111\/j.1572-0241.2004.30110.x","article-title":"Evaluation of liver fibrosis: A concise review","volume":"99","author":"Afdhal","year":"2004","journal-title":"Off. J. Am. Coll. Gastroenterol. ACG"},{"key":"ref_15","unstructured":"Chen, H. (2020). Integrated Study of Liver Fibrosis: Modeling and Clinical Detection, Georgia State University."},{"key":"ref_16","unstructured":"Gamal Mohamed Abdelall, N. (2015). Accuracy of liVer Stiffness Measurement Using Fibroscan\u00ae to Predict the Response to Antiviral Therapy in Patients with Chronic Hepatitis c Viral Infection. [Ph.D. Thesis, Alma Mater Studiorum Universit\u00e0 di Bologna]."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1038\/nrclinonc.2013.101","article-title":"Biopsies: Next-generation biospecimens for tailoring therapy","volume":"10","author":"Basik","year":"2013","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e2100337","DOI":"10.1200\/GO.21.00337","article-title":"Importance of cytopathologic diagnosis in early cancer diagnosis in resource-constrained countries","volume":"8","author":"Yadav","year":"2022","journal-title":"JCO Glob. Oncol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/s41377-023-01104-7","article-title":"Deep learning-enabled virtual histological staining of biological samples","volume":"12","author":"Bai","year":"2023","journal-title":"Light. Sci. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4291","DOI":"10.1111\/bph.15633","article-title":"Artificial intelligence for solid tumour diagnosis in digital pathology","volume":"178","author":"Klein","year":"2021","journal-title":"Br. J. Pharmacol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"S22","DOI":"10.1016\/j.jhep.2004.12.008","article-title":"Fibrosis in chronic liver diseases: Diagnosis and management","volume":"42","author":"Pinzani","year":"2005","journal-title":"J. Hepatol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102537","DOI":"10.1016\/j.media.2022.102537","article-title":"Conditional GANs based system for fibrosis detection and quantification in Hematoxylin and Eosin whole slide images","volume":"81","author":"Naglah","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_23","first-page":"10","article-title":"Histological stains in the past, present, and future","volume":"13","author":"Javaeed","year":"2021","journal-title":"Cureus"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101997","DOI":"10.1016\/j.inffus.2023.101997","article-title":"Stain normalization methods for histopathology image analysis: A comprehensive review and experimental comparison","volume":"102","author":"Hoque","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1002\/gcc.23177","article-title":"Machine learning in computational histopathology: Challenges and opportunities","volume":"62","author":"Cooper","year":"2023","journal-title":"Genes Chromosom. Cancer"},{"key":"ref_26","unstructured":"Orchard, G., and Nation, B. (2012). Histopathology, Oxford University Press."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/jmri.27409","article-title":"Histological validation of MRI: A review of challenges in registration of imaging and whole-mount histopathology","volume":"55","author":"Alyami","year":"2022","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1054\/mehy.1999.0894","article-title":"Limitations of clinical and biological histology","volume":"54","author":"Hillman","year":"2000","journal-title":"Med. Hypotheses"},{"key":"ref_29","unstructured":"Yousefi, J. (2011). Image Binarization Using Otsu Thresholding Algorithm. [Ph.D. Thesis, Cape Breton University]."},{"key":"ref_30","unstructured":"Naveen, C. (2026, February 25). SIFT (Scale invariant Feature Transform) Algorithm. Available online: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/43723-sift-scale-invariant-feature-transform-algorithm."},{"key":"ref_31","unstructured":"Lindeberg, T. (2025, August 01). Scale Invariant Feature Transform. Available online: https:\/\/www.diva-portal.org\/smash\/get\/diva2:480321\/FULLTEXT02."},{"key":"ref_32","unstructured":"Viswanathan, D.G. (2009, January 6\u20138). Features from accelerated segment test (fast). Proceedings of the 10th Workshop on Image Analysis for Multimedia Interactive Services, London, UK."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). Brief: Binary robust independent elementary features. Proceedings of the Computer Vision\u2013ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, 5\u201311 September 2010, Springer. Proceedings, Part IV 11.","DOI":"10.1007\/978-3-642-15561-1_56"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Raguram, R., Frahm, J.M., and Pollefeys, M. (2009). Exploiting uncertainty in random sample consensus. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2009.5459456"},{"key":"ref_35","unstructured":"Chum, O. (2005). Two-View Geometry Estimation by Random Sample and Consensus. [Ph.D. Thesis, Colorado Technical University]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"228","DOI":"10.2514\/1.J055102","article-title":"Comparison of B-spline surface and free-form deformation geometry control for aerodynamic optimization","volume":"55","author":"Lee","year":"2017","journal-title":"AIAA J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tustison, N.J., Avants, B.A., and Gee, J.C. (2007). Improved FFD B-spline image registration. Proceedings of the 2007 IEEE 11th International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2007.4409161"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"024001","DOI":"10.1115\/1.2906694","article-title":"Freeform deformation versus B-spline representation in inverse airfoil design","volume":"8","author":"Amoiralis","year":"2008","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Peters, J., Lebrat, L., Cruz, R.S., Nicolson, A., Belous, G., Konate, S., Raniga, P., Dore, V., Bourgeat, P., and Mejan-Fripp, J. (2023). DBCE: A saliency method for medical deep learning through anatomically-consistent free-form deformations. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, IEEE.","DOI":"10.1109\/WACV56688.2023.00200"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TVCG.2004.1260760","article-title":"Image registration using hierarchical B-splines","volume":"10","author":"Xie","year":"2004","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s11071-012-0709-3","article-title":"Use of B-spline surface to model large-deformation continuum plates: Procedure and applications","volume":"72","author":"Nada","year":"2013","journal-title":"Nonlinear Dyn."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105506","DOI":"10.1016\/j.cmpb.2020.105506","article-title":"Stain Color Adaptive Normalization (SCAN) algorithm: Separation and standardization of histological stains in digital pathology","volume":"193","author":"Salvi","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_43","first-page":"012108","article-title":"Stain removal through color normalization of haematoxylin and eosin images: A review","volume":"Volume 1362","author":"Lakshmanan","year":"2019","journal-title":"Proceedings of the Journal of Physics: Conference Series"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/38.946629","article-title":"Color transfer between images","volume":"21","author":"Reinhard","year":"2001","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_45","first-page":"15475","article-title":"Rest: An efficient transformer for visual recognition","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yuan, K., Guo, S., Liu, Z., Zhou, A., Yu, F., and Wu, W. (2021, January 10\u201317). Incorporating convolution designs into visual transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00062"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Srivastava, A., Chandra, M., Saha, A., Saluja, S., and Bhati, D. (2023). Current Advances in Locality-Based and Feature-Based Transformers: A Review. Proceedings of the International Conference on Data & Information Sciences, Springer.","DOI":"10.1007\/978-981-99-6906-7_28"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/S0360-8352(00)00011-5","article-title":"Relative performances of similarity and dissimilarity measures","volume":"37","author":"Sarker","year":"1999","journal-title":"Comput. Ind. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/TIP.2013.2251645","article-title":"Structural texture similarity metrics for image analysis and retrieval","volume":"22","author":"Zujovic","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/34.790428","article-title":"Similarity measures","volume":"21","author":"Santini","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/ACVMOT.2005.71","article-title":"Measures of similarity","volume":"Volume 1","author":"Unnikrishnan","year":"2005","journal-title":"Proceedings of the 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV\/MOTION\u201905)-Volume 1"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Thung, K.H., and Raveendran, P. (2009). A survey of image quality measures. Proceedings of the 2009 International Conference for Technical Postgraduates (TECHPOS), IEEE.","DOI":"10.1109\/TECHPOS.2009.5412098"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"116087","DOI":"10.1016\/j.eswa.2021.116087","article-title":"Structural similarity index (SSIM) revisited: A data-driven approach","volume":"189","author":"Bakurov","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/97.995823","article-title":"A universal image quality index","volume":"9","author":"Wang","year":"2002","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1023\/A:1026543900054","article-title":"The earth mover\u2019s distance as a metric for image retrieval","volume":"40","author":"Rubner","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Farid, H. (2021). An overview of perceptual hashing. J. Online Trust Saf., 1.","DOI":"10.54501\/jots.v1i1.24"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhang, X., Delpha, C., and Diallo, D. (2019). Performance of Jensen Shannon divergence in incipient fault detection and estimation. Proceedings of the ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.","DOI":"10.1109\/ICASSP.2019.8682206"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mittal, A., Moorthy, A.K., and Bovik, A.C. (2011). Blind\/referenceless image spatial quality evaluator. Proceedings of the 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), IEEE.","DOI":"10.1109\/ACSSC.2011.6190099"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/764\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T09:11:03Z","timestamp":1772615463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"references-count":58,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["diagnostics16050764"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16050764","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]}}}