Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining

Kang, Lei; Li, Xiufeng; Zhang, Yan; Wong, Terence T. W.

doi:10.1016/j.pacs.2021.100308

Cited by 33 publications

(33 citation statements)

References 38 publications

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“…Sample preprocessing workflows are important for DL models [129]. Formalin-fixed, paraffin-embedded (FFPE) sections are the most common tissue samples in histopathology, resulting in high-quality images that may take days to be prepared, while frozen sections are the tissue samples that are obtained in urgent situations for a rapid overview of the tumor, providing images within less than an hour, albeit being more prone to the artifacts and disturbances in morphology [130,131]. Most DL studies use FFPE sections [81], but some studies have demonstrated good performance on frozen sections [37].…”

Section: Quality Control and Preprocessing Protocolsmentioning

confidence: 99%

Artificial intelligence to identify genetic alterations in conventional histopathology

Cifci

Foersch

Kather

2022

The Journal of Pathology

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Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53, and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows.

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Section: Quality Control and Preprocessing Protocolsmentioning

confidence: 99%

Artificial intelligence to identify genetic alterations in conventional histopathology

Cifci

Foersch

Kather

2022

The Journal of Pathology

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“…Developing these stained FFPE specimens for brightfield microscopic assessment relies on a laborious process of fixation, embedding, sectioning, and staining 3 , a procedure which takes several days to complete 4 . This lengthy process has motivated the adoption of frozen section (FS) histology, a technique which is commonly used for rapid intraoperative assessment.…”

Section: Introductionmentioning

confidence: 99%

Virtual histological staining of label-free total absorption photoacoustic remote sensing (TA-PARS)

Boktor

Ecclestone

Pekar

et al. 2022

Sci Rep

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Histopathological visualizations are a pillar of modern medicine and biological research. Surgical oncology relies exclusively on post-operative histology to determine definitive surgical success and guide adjuvant treatments. The current histology workflow is based on bright-field microscopic assessment of histochemical stained tissues and has some major limitations. For example, the preparation of stained specimens for brightfield assessment requires lengthy sample processing, delaying interventions for days or even weeks. Therefore, there is a pressing need for improved histopathology methods. In this paper, we present a deep-learning-based approach for virtual label-free histochemical staining of total-absorption photoacoustic remote sensing (TA-PARS) images of unstained tissue. TA-PARS provides an array of directly measured label-free contrasts such as scattering and total absorption (radiative and non-radiative), ideal for developing H&E colorizations without the need to infer arbitrary tissue structures. We use a Pix2Pix generative adversarial network to develop visualizations analogous to H&E staining from label-free TA-PARS images. Thin sections of human skin tissue were first virtually stained with the TA-PARS, then were chemically stained with H&E producing a one-to-one comparison between the virtual and chemical staining. The one-to-one matched virtually- and chemically- stained images exhibit high concordance validating the digital colorization of the TA-PARS images against the gold standard H&E. TA-PARS images were reviewed by four dermatologic pathologists who confirmed they are of diagnostic quality, and that resolution, contrast, and color permitted interpretation as if they were H&E. The presented approach paves the way for the development of TA-PARS slide-free histological imaging, which promises to dramatically reduce the time from specimen resection to histological imaging.

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“…The ability to virtually stain microscopic images of unlabeled tissue sections was demonstrated through deep neural networks, avoiding the laborious and time-consuming histochemical staining processes. These deep learning-based label-free virtual staining methods can use different input imaging modalities, such as autofluorescence microscopy [2][3][4], hyperspectral imaging [5], quantitative phase imaging (QPI) [6], reflectance confocal microscopy [7], and photoacoustic microscopy [8], among others [9][10][11]. Virtual staining, in general, has the potential to be used as a substitute for histochemical staining, providing savings in both costs and tissue processing time.…”

Section: Introductionmentioning

confidence: 99%

Virtual Staining of Defocused Autofluorescence Images of Unlabeled Tissue Using Deep Neural Networks

et al. 2022

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Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue. Standard virtual staining requires high autofocusing precision during the whole slide imaging of label-free tissue, which consumes a significant portion of the total imaging time and can lead to tissue photodamage. Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to virtual staining of in-focus label-free images, also saving significant imaging time by lowering the microscope’s autofocusing precision. This framework incorporates a virtual autofocusing neural network to digitally refocus the defocused images and then transforms the refocused images into virtually stained images using a successive network. These cascaded networks form a collaborative inference scheme: the virtual staining model regularizes the virtual autofocusing network through a style loss during the training. To demonstrate the efficacy of this framework, we trained and blindly tested these networks using human lung tissue. Using 4× fewer focus points with 2× lower focusing precision, we successfully transformed the coarsely-focused autofluorescence images into high-quality virtually stained H&E images, matching the standard virtual staining framework that used finely-focused autofluorescence input images. Without sacrificing the staining quality, this framework decreases the total image acquisition time needed for virtual staining of a label-free whole-slide image (WSI) by ~32%, together with a ~89% decrease in the autofocusing time, and has the potential to eliminate the laborious and costly histochemical staining process in pathology.

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Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining

Cited by 33 publications

References 38 publications

Artificial intelligence to identify genetic alterations in conventional histopathology

Artificial intelligence to identify genetic alterations in conventional histopathology

Virtual histological staining of label-free total absorption photoacoustic remote sensing (TA-PARS)

Virtual Staining of Defocused Autofluorescence Images of Unlabeled Tissue Using Deep Neural Networks

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