A low-cost pathological image digitalization method based on 5 times magnification scanning.

Abstract

Digital pathology has aroused widespread interest in modern pathology. The key to digitalization is to scan the whole slide image (WSI) at high magnification. The file size of each WSI at 40 times magnification (40×) may range from 1 gigabyte (GB) to 5 GB depending on the size of the specimen, which leads to huge storage capacity, very slow scanning and network exchange, seriously increasing time and storage costs for digital pathology. We design a strategy to scan slides with low resolution (LR) (5×), and a superresolution (SR) method is proposed to restore the image details during diagnosis. The method is based on a multiscale generative adversarial network, which can sequentially generate three high-resolution (HR) images: 10×, 20×, and 40×. A dataset consisting of 100,000 pathological images from 10 types of human body systems is used for training and testing. The differences between the generated images and the real images have been extensively evaluated using quantitative evaluation, visual inspection, medical scoring, and diagnosis. The file size of each 5× WSI is approximately 15 Megabytes. The peak-signal-to-noise ratios (PSNRs) of 10× to 40× generated images are 24.167±3.734 dB, 22.272±4.272 dB, and 20.436±3.845 dB, and the structural similarity (SSIM) index values are 0.845±0.089, 0.680±0.150, and 0.559±0.179, which are better than those of other SR networks and conventional digital zoom methods. Visual inspections show that the generated images have details similar to the real images. Visual scoring average with 0.95 confidence interval from three pathologists are 3.630±1.024, 3.700±1.126, and 3.740±1.095, respectively, and the P value of analysis of variance is 0.367, indicating the pathologists confirm that generated images include sufficient information for diagnosis. The average value of the Kappa test of the diagnoses of paired generated and real images is 0.990, meaning the diagnosis of generated images is highly consistent with that of the real images. The proposed method can generate high-quality 10×, 20×, 40× images from 5× images, which can effectively reduce the time and storage costs of digitalization up to 1/64 of the previous costs, which shows the potential for clinical applications and is expected to be an alternative digitalization method after large-scale evaluation.