组织学病理图像在深度学习中染色处理的研究进展
[1] JOSE L, LIU S, RUSSO C, et al. Generative adversarial networks in digital pathology and histopathological image processing: a review [J]. Journal of Pathology Informatics, 2021, 12: 43.
[2] BIANCONI F, KATHER J N, REYES-ALDASORO C C. Experimental assessment of color deconvolution and color normalization for automated classification of histology images stained with hematoxylin and eosin [J]. Cancers, 2020, 12(11): 3337.
[3] REINHARD E, ADHIKHMIN M, GOOCH B, et al. Color transfer between images [J]. IEEE Computer Graphics and Applications, 2001, 21(4): 34-41.
[4] ROY S, PANDA S, JANGID M. Modified reinhard algorithm for color normalization of colorectal cancer histopathology images [C]. 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021: 1231-1235
[5] ASWATHY M A, JAGANNATH M. Dual stage normalization approach towards classification of breast cancer [J]. IETE Journal of Research, 2022, 68(4): 3074-3085
[6] NADEEM S, HOLLMANN T, TANNENBAUM A. Multimarginal Wasserstein barycenter for stain normalization and augmentation [C]. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2020: 362-371.
[7] RUIFROK A C, KATZ R L, JOHNSTON D A. Comparison of quantification of histochemical staining by hue-saturation-intensity (HSI) transformation and color-deconvolution [J]. Applied Immunohistochemistry & Molecular Morphology, 2003, 11(1): 85-91.
[8] ZHENG Y, JIANG Z, ZHANG H, et al. Adaptive color deconvolution for histological WSI normalization[J]. Computer Methods and Programs in Biomedicine, 2019, 170: 107-120.
[9] SALVI M, MICHIELLI N, MOLINARI F. Stain color adaptive normalization (SCAN) algorithm: separation and standardization of histological stains in digital pathology [J]. Computer Methods and Programs in Biomedicine, 2020, 193: 105506.
[10] MACENKO M, NIETHAMMER M, MARRON J S, et al. A method for normalizing histology slides for quantitative analysis [C]. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2009: 1107-1110.
[11] VAHADANE A, PENG T, SETHI A, et al. Structure-preserving color normalization and sparse stain separation for histological images [J]. IEEE Transactions on Medical Imaging, 2016, 35(8): 1962-1971.
[12] ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks [C]. Proceedings of the IEEE International Conference on Computer Vision, 2017: 2223-2232.
[13] JIAO Y, LI J, FEI S. Staining condition visualization in digital histopathological whole-slide images [J]. Multimedia Tools and Applications, 2022, 81(13): 17831-17847.
[14] SHABAN M T, BAUR C, NAVAB N, et al. StainGAN: stain style transfer for digital histological images [C]. 2019 Ieee 16th international symposium on biomedical imaging (Isbi 2019). IEEE, 2019: 953-956.
[15] KANG H, LUO D, FENG W, et al. StainNet: a fast and robust stain normalization network[J]. Frontiers in Medicine, 2021, 8: 746307.
[16] GUTIERREZ PEREZ J C, OTERO BAGUER D, MAASS P. StainCUT: stain normalization with contrastive learning [J]. Journal of Imaging, 2022, 8(7): 202.
[17] PARK T, EFROS A A, ZHANG R, et al. Contrastive learning for unpaired image-to-image translation [C]. European Conference on Computer Vision. Springer, Cham, 2020: 319-345.
[18] MAO X, WANG J, TAO X, et al. Single generative networks for stain normalization and quality enhancement of histological images in digital pathology [C]. 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2021: 1-5.
[19] NAZKI H, ARANDJELOVI? O, UM I H, et al. MultiPathGAN: structure preserving stain normalization using unsupervised multi-domain adversarial network with perception loss [J]. arXiv, 2022: 2204.09782. https://doi.org/10.48550/arXiv:2204.09782.
[20] SHRIVASTAVA A, ADORNO W, SHARMA Y, et al. Self-attentive adversarial stain normalization[C]. International Conference on Pattern Recognition. Springer, Cham, 2021: 120-140.
[21] LIU Y, YUAN H, WANG Z, et al. Global pixel transformers for virtual staining of microscopy images [J]. IEEE Transactions on Medical Imaging, 2020, 39(6): 2256-2266.
[22] CHRISTIANSEN E M, YANG S J, ANDO D M, et al. In silico labeling: predicting fluorescent labels in unlabeled images [J]. Cell, 2018, 173(3): 792-803.e19.
[23] SIMONSON P D, REN X, FROMM J R. Creating virtual hematoxylin and eosin images using samples imaged on a commercial CODEX platform [J]. Journal of Pathology Informatics, 2021, 12(1): 52.
[24] BURLINGAME E A, MCDONNELL M, SCHAU G F, et al. SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning [J]. Scientific Reports, 2020, 10(1): 17507.
[25] Rivenson Y, Wang H, Wei Z, et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning[J]. Nature Biomedical Engineering, 2019, 3(6): 466-477.
[26] CHEN Z, YU W, WONG I H M, et al. Deep-learning-assisted microscopy with ultraviolet surface excitation for rapid slide-free histological imaging [J]. Biomedical Optics Express, 2021, 12(9): 5920.
[27] LI J, GARFINKEL J, ZHANG X, et al. Biopsy-free in vivo virtual histology of skin using deep learning [J]. Light: Science & Applications, 2021, 10(1): 233.
[28] ZHANG R, CAO Y, LI Y, et al. MVFStain: multiple virtual functional stain histopathology images generation based on specific domain mapping [J]. Medical Image Analysis, 2022, 80: 102520.
[29] DE HAAN K, ZHANG Y, ZUCKERMAN J E, et al. Deep learning-based transformation of H&E stained tissues into special stains [J]. Nature Communications, 2021, 12(1): 4884.
[30] BELL K, ABBASI S, DINAKARAN D, et al. Reflection-mode virtual histology using photoacoustic remote sensing microscopy [J]. Scientific Reports, 2020, 10(1): 1-13.
[31] KANG L, LI X, ZHANG Y, et al. Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining [J]. Photoacoustics, 2022, 25: 100308.
[32] MARTELL M T, HAVEN N J M, MCALISTER E A, et al. Deep learning-enabled realistic virtual histology with ultraviolet scattering and photoacoustic remote sensing microscopy [C]. Microscopy Histopathology and Analytics. Optica Publishing Group, 2022: MS4A.2.
[33] GAREAU D S, JEON H, NEHAL K S, et al. Rapid screening of cancer margins in tissue with multimodal confocal microscopy [J]. Journal of Surgical Research, 2012, 178(2): 533-538.
[34] CARRASCO-ZEVALLOS O M, VIEHLAND C, KELLER B, et al. Review of intraoperative optical coherence tomography: technology and applications [Invited][J]. Biomedical Optics Express, 2017, 8(3): 1607-1637.
[35] RESTALL B S, CIKALUK B D, MARTELL M T, et al. Fast hybrid optomechanical scanning photoacoustic remote sensing microscopy for virtual histology [J]. Biomedical Optics Express, 2022, 13(1): 39-47.
[36] HOLLANDI R, SZKALISITY A, TOTH T, et al. NucleAIzer: a parameter-free deep learning framework for nucleus segmentation using image style transfer [J]. Cell Systems, 2020, 10(5): 453-458.e6.
[37] HUANG X, LIU M Y, BELONGIE S, et al. Multimodal unsupervised image-to-image translation [C]. Proceedings of the European Conference on Computer Vision (ECCV), 2018: 172-189.
[38] WANG H, ZHENG H, CHEN J, et al. Unlabeled data guided semi-supervised histopathology image segmentation [C]. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020: 815-820.
[39] YAMASHITA R, LONG J, BANDA S, et al. Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation [J]. IEEE Transactions on Medical Imaging, 2021, 40(12): 3945-3954.
[40] LIU Y, WAGNER S J, PENG T. Multi-modality microscopy image style augmentation for nuclei segmentation [J]. Journal of Imaging, 2022, 8(3): 71.
[41] MOGHADAM A Z, AZARNOUSH H, SEYYEDSALEHI S A, et al. Stain transfer using Generative Adversarial Networks and disentangled features [J]. Computers in Biology and Medicine, 2022, 142: 105219.
罗诗欢, 刘智明, 杨必文, 郭周义. 组织学病理图像在深度学习中染色处理的研究进展[J]. 激光生物学报, 2022, 31(6): 481. LUO Shihuan, LIU Zhiming, YANG Biwen, GUO Zhouyi. Advances in Staining Processing of Histological Pathology Images in Deep Learning[J]. Acta Laser Biology Sinica, 2022, 31(6): 481.