Objective:This article describes a new method(VS-FPM)for analysis of unstained tissues based on the application of supervised machine learning to generate brightfield hematoxylin and eosin(H&E)images from phase im...Objective:This article describes a new method(VS-FPM)for analysis of unstained tissues based on the application of supervised machine learning to generate brightfield hematoxylin and eosin(H&E)images from phase images recovered using Fourier ptychographic microscopy(FPM).Impact Statement:VS-FPM has several advantages for label-free digital pathology.Capture of complex image information simplifies model training and allows post-capture refocusing.FPM images combine high resolution with a large field of view,and the hardware is low-cost and compatible with many existing brightfield microscope systems.Introduction:By generating realistic histologically stained images from label-free image data,virtual staining(VS)methods have the potential to streamline clinical workflows,improve image consistency,and enable new ways of visualizing and analyzing histological tissues.Methods:We trained a conditional generative adversarial network to translate high-resolution FPM images of unstained tissues to brightfield H&E images and assessed the method using diagnosis of colonic polyps as a test case.Results:We found no statistically significant difference between the spatial resolution of FPM images captured at 4×magnification and images from a pathology slide scanner at 20×magnification.Visual assessment and image similarity metrics showed that VS-FPM images of unstained tissues closely resemble images of chemically H&Estained tissues.However,the spatial resolution of virtual H&E images was approximately 20%lower than equivalent images of chemically stained tissues.Using VS-FPM,board-certified pathologists were able to accurately distinguish normal from dysplastic tissues and derive correct pathological diagnoses.Conclusion:VS-FPM is a reliable,accessible VS method that also overcomes many other limitations inherent to histopathology microscopy.展开更多
基金funded by a proof-of-concept award from Wellcome/EPSRC Centre for Interventional and Surgical Science(award 203145Z/16/Z)the NIHR UCLH Biomedical Research Centre(award 187809).A.P.L.was funded by The Pathological Society of Great Britain and Ireland.
文摘Objective:This article describes a new method(VS-FPM)for analysis of unstained tissues based on the application of supervised machine learning to generate brightfield hematoxylin and eosin(H&E)images from phase images recovered using Fourier ptychographic microscopy(FPM).Impact Statement:VS-FPM has several advantages for label-free digital pathology.Capture of complex image information simplifies model training and allows post-capture refocusing.FPM images combine high resolution with a large field of view,and the hardware is low-cost and compatible with many existing brightfield microscope systems.Introduction:By generating realistic histologically stained images from label-free image data,virtual staining(VS)methods have the potential to streamline clinical workflows,improve image consistency,and enable new ways of visualizing and analyzing histological tissues.Methods:We trained a conditional generative adversarial network to translate high-resolution FPM images of unstained tissues to brightfield H&E images and assessed the method using diagnosis of colonic polyps as a test case.Results:We found no statistically significant difference between the spatial resolution of FPM images captured at 4×magnification and images from a pathology slide scanner at 20×magnification.Visual assessment and image similarity metrics showed that VS-FPM images of unstained tissues closely resemble images of chemically H&Estained tissues.However,the spatial resolution of virtual H&E images was approximately 20%lower than equivalent images of chemically stained tissues.Using VS-FPM,board-certified pathologists were able to accurately distinguish normal from dysplastic tissues and derive correct pathological diagnoses.Conclusion:VS-FPM is a reliable,accessible VS method that also overcomes many other limitations inherent to histopathology microscopy.
