Development of Pathological Super-resolution Images using Artificial Intelligence based on Whole Slide Image

Abstract

Background: Pathology plays a very important role in the cancer diagnosis, as the gold standard for the identification of tumors. The rapid development of digital pathology (DP) which based on Whole Slide Image (WSI) has led to many improvements in telepathological consultation, digital management, and computer-assisted diagnosis by artificial intelligence (AI). In DP, the common digitization strategy is to scan the pathology slice with X20 or X40 objective. Usually, the X40's WSI is 4 times bigger than the X20's, and obviously, the storage space and transmission time of the data should be 4 times. These increased costs will be great negative factor in the popularization of DP. But at the same time, some cases have to use the high magnification WSI for reliable diagnosis. Methods: In this article, we present a novel super-resolution process which could be used for WSI through Deep Learning. This process powered by AI, have the ability to switch X20 WSI to X40 without loss of whole and locally features. Furthermore, we collect the examples of WSI data of patients with 100 uterine leiomyosarcoma and adult granulosa cell tumor (AGCT) of ovary respectively, which are used to test our super-resolution process. Results: We used the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM), and the Blind/Referenceless Image Spatial QUality Evaluator (BRISQUE) to test the resulting X40 WSI synthesized by the super-resolution (SR), which were 42.03, 0.99 and 49.22 . Then, we tested our SR images from subjective evaluation of the pathologist's perspective, and tested that if the pathologists could objectively distinguish the images between SR and high-resolution (HR), to further confirm the consistency between our SR images and the real HR images. Conclusions: The testing results indicate that the X40 WSI synthesized by the super-resolution matches the performance of the one generated from the X40 objective in diagnosis of both tumors. We believe that this is a reliable method can be used in a variety of tumors' digital slides, and will be available for a large scale in clinical pathology as an innovative technique.