PhaseStain:the digital staining of label-free quantitative phase microscopy images using deep learning

Using a deep neural network,we demonstrate a digital staining technique,which we term PhaseStain,to transform the quantitative phase images(QPI)of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained.Through pairs of image data(QPI and the corresponding brightfield images,acquired after staining),we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin,kidney,and liver tissue,matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin,Jones’stain,and Masson’s trichrome stain,respectively.This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general,by eliminating the need for histological staining,reducing sample preparation related costs and saving time.Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning.

Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning

Parasitic infections constitute a major global public health issue.Existing screening methods that are based on manual microscopic examination often struggle to provide sufficient volumetric throughput and sensitivity to facilitate early diagnosis.Here,we demonstrate a motility-based label-free computational imaging platform to rapidly detect motile parasites in optically dense bodily fluids by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism.Based on this principle,a cost-effective and mobile instrument,which rapidly screens~3.2 mL of fluid sample in three dimensions,was built to automatically detect and count motile microorganisms using their holographic time-lapse speckle patterns.We demonstrate the capabilities of our platform by detecting trypanosomes,which are motile protozoan parasites,with various species that cause deadly diseases affecting millions of people worldwide.Using a holographic speckle analysis algorithm combined with deep learningbased classification,we demonstrate sensitive and label-free detection of trypanosomes within spiked whole blood and artificial cerebrospinal fluid(CSF)samples,achieving a limit of detection of ten trypanosomes per mL of whole blood(~five-fold better than the current state-of-the-art parasitological method)and three trypanosomes per mL of CSF.We further demonstrate that this platform can be applied to detect other motile parasites by imaging Trichomonas vaginalis,the causative agent of trichomoniasis,which affects 275 million people worldwide.With its costeffective,portable design and rapid screening time,this unique platform has the potential to be applied for sensitive and timely diagnosis of neglected tropical diseases caused by motile parasites and other parasitic infections in resource-limited regions.