A generative adversarial approach to facilitate archivalquality histopathologic diagnoses from frozen tissue sections

08/25/2021

∙

In clinical diagnostics and research involving histopathology, formalin fixed paraffin embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (>24 hours) can slow decision-making. In contrast, fresh frozen (FF) processing (<1 hour) can yield rapid information but diagnostic accuracy is suboptimal due to lack of clearing, morphologic deformation and more frequent artifacts. Here, we bridge this gap using artificial intelligence. We synthesize FFPE-like images (“virtual FFPE”) from FF images using a generative adversarial network (GAN) from 98 paired kidney samples derived from 40 patients. Five boardcertified pathologists evaluated the results in a blinded test. Image quality of the virtual FFPE data was assessed to be high and showed a close resemblance to real FFPE images. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. The nearly instantaneously generated virtual FFPE images can not only reduce time to information but can facilitate more precise diagnosis from routine FF images without extraneous costs and effort.

READ FULL TEXT

A generative adversarial approach to facilitate archivalquality histopathologic diagnoses from frozen tissue sections

VITON-GAN: Virtual Try-on Image Generator Trained with Adversarial Loss

Deep Learning-based Frozen Section to FFPE Translation

Deepfake histological images for enhancing digital pathology

Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network

Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning

Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networks

Ensuring accurate stain reproduction in deep generative networks for virtual immunohistochemistry

A generative adversarial approach to facilitate archivalquality histopathologic diagnoses from frozen tissue sections

Related Research

VITON-GAN: Virtual Try-on Image Generator Trained with Adversarial Loss

Deep Learning-based Frozen Section to FFPE Translation

Deepfake histological images for enhancing digital pathology

Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network

Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning

Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networks

Ensuring accurate stain reproduction in deep generative networks for virtual immunohistochemistry