A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded

Ozyoruk, Kutsev Bengisu; Can, Sermet; Darbaz, Berkan; Başak, Kayhan; Demır, Derya; Gokceler, Guliz Irem; Serın, Gürdenız; Hacısalihoğlu, Uğuray Payam; Kurtuluş, Emirhan; Lu, Ming; Chen, Tiffany; Williamson, Drew F. K.; Yılmaz, Funda; Mahmood, Faisal; Turan, Mehmet

doi:10.1038/s41551-022-00952-9

Cited by 32 publications

(42 citation statements)

References 36 publications

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“…The integration of artificial intelligence (AI) with various microscopy techniques has revolutionized the fields of biomedical photonics, enabling the tasks of breaking the limits of image resolution and speed (13,14), diagnostic classification (15)(16)(17)(18), semantic segmentation (19)(20)(21)(22), cross-modality transformation (23,24), and virtual staining (5,(25)(26)(27), etc. In particular, virtual histologic stains could be converted from unlabeled UV-autofluorescence (27) and UV-PAM images (5), and virtual FFPE could be transformed from frozen H&E results via deep learning (26). These methods exploit the power of generative adversarial networks (GANs), a type of deep-learning model that is used in the translation between two relevant domains (28)(29)(30).…”

Section: Introductionmentioning

confidence: 99%

“…For slide-free images of fresh tissues, weakly supervised CycleGAN architectures are more suited to generate virtual histologic images without the need of well-aligned image pairs (5,31). However, generic CycleGAN is still insufficient to achieve FFPE-quality images, even for the translation from similar-styled frozen H&E sections (26). It is much more challenging to transform SRS images of unprocessed tissues to FFPE style.…”

Section: Introductionmentioning

confidence: 99%

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Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model

Liu,

Chen,

Cheng

et al. 2024

Sci. Adv.

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Intraoperative histology is essential for surgical guidance and decision-making. However, frozen-sectioned hematoxylin and eosin (H&E) staining suffers from degraded accuracy, whereas the gold-standard formalin-fixed and paraffin-embedded (FFPE) H&E is too lengthy for intraoperative use. Stimulated Raman scattering (SRS) microscopy has shown rapid histology of brain tissue with lipid/protein contrast but is challenging to yield images identical to nucleic acid–/protein-based FFPE stains interpretable to pathologists. Here, we report the development of a semi-supervised stimulated Raman CycleGAN model to convert fresh-tissue SRS images to H&E stains using unpaired training data. Within 3 minutes, stimulated Raman virtual histology (SRVH) results that matched perfectly with true H&E could be generated. A blind validation indicated that board-certified neuropathologists are able to differentiate histologic subtypes of human glioma on SRVH but hardly on conventional SRS images. SRVH may provide intraoperative diagnosis superior to frozen H&E in both speed and accuracy, extendable to other types of solid tumors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model

Liu,

Chen,

Cheng

et al. 2024

Sci. Adv.

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“…Despite the growing number of digital pathology and AI studies in our country, this progress is restricted by the digitization of pathology slides and AI-oriented technical facilities (16). Although a small number of articles (17) with focus on pathology and pathologists' participation are included in national publication directories, many Turkish researchers are involved in international studies and publications (8)(9)(10)12,(18)(19)(20), and the number of publications from Turkey has been increasing (21)(22)(23)(24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

Whole slide images in artificial intelligence applications in digital pathology: challenges and pitfalls

Kayhan¹,

Ozyoruk²,

Demır³

2023

TJPATH

Self Cite

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The use of digitized data in pathology research is rapidly increasing. The whole slide image (WSI) is an indispensable part of the visual examination of slides in digital pathology and artificial intelligence applications; therefore, the acquisition of WSI with the highest quality is essential. Unlike the conventional routine of pathology, the digital conversion of tissue slides and the differences in its use pose difficulties for pathologists. We categorized these challenges into three groups: before, during, and after the WSI acquisition. The problems before WSI acquisition are usually related to the quality of the glass slide and reflect all existing problems in the analytical process in pathology laboratories. WSI acquisition problems are dependent on the device used to produce the final image file. They may be related to the parts of the device that create an optical image or the hardware and software that enable digitization. Post-WSI acquisition issues are related to the final image file itself, which is the final form of this data, or the software and hardware that will use this file. Because of the digital nature of the data, most of the difficulties are related to the capabilities of the hardware or software. Being aware of the challenges and pitfalls of using digital pathology and AI will make pathologists' integration to the new technologies easier in their daily practice or research.

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“…33 Given a large enough repository of diagnostic WSIs (n > 100), 34 deep learning can be used to formulate and solve new clinical tasks beyond human pathologist capabilities such as metastatic origin of cancer prediction, 35 cancer prognostication, [36][37][38][39][40] and microsatellite instability (MSI) prediction. [41][42][43] Looking beyond supervised learning applications via CNNs and MIL, the development of other techniques such as generative AI modeling, [44][45][46] geometric deep learning, 47,48 unsupervised learning, 49,50 and multimodal deep learning 51 may soon enable new clinical capabilities that could enter pathology and laboratory medicine workflows, such as virtual staining, [52][53][54] elucidating cell and tissue interactions, 55 untargeted biomarker discovery, 56,57 data fusion with genomics and other rich biomedical data streams. [58][59][60][61] Though direct application of many deep learning techniques may appear to work "out-of-the-box" and have emergent capabilities in computational pathology (CPATH), there exists a variety of technical challenges that would limit their adoption in clinical translation, deployment, and commercialization.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in computational histopathology: Challenges and opportunities

Cooper

Krishnan

2023

Genes Chromosomes & Cancer

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Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.

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A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded

Cited by 32 publications

References 36 publications

Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model

Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model

Whole slide images in artificial intelligence applications in digital pathology: challenges and pitfalls

Machine learning in computational histopathology: Challenges and opportunities

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