Accuracy improvement of quantification information using super-resolution with convolutional neural network for microscopy images

Abstract

Background and Objective: Microscope images are used for cell biology and clinical analysis. In general, microscopic images of 10x magnification are frequently used for cell imaging because of environmental limitations such as reagent drying, photo-bleaching, and photo-toxicity. However, there is a limit to the image quality of a 10x image to obtain more accurate information. Therefore, it is necessary to improve the image quality. Methods: In this paper, we propose a novel method to improve quantification accuracy using a super-resolution with a convolutional neural network (CNN) with image-based cell phenotypic profiling to predict the responses of glioblastoma cells to a drug using automatic image processing. For this approach, we first generate 40x high-quality images from originally obtained 10x images using a CNN-based method. Next, we manually obtain segmented images from three experts as ground-truth images to evaluate the quantitative improvement of segmentation. Intensity-based automatic segmentation results for cell nuclei morphological features for the 10x original images and CNN-based 40x images are compared with the ground-truth images. Results: The segmentation accuracy of the CNN-based 40x images is more similar to that of the manual segmenting results than that of the 10x images, as the Sore . nsen-Dice similarity coefficient. In addition, the CNN-based 40x image results are more similar to those . of the manual results than those of the 10x images. Conclusions: We confirmed that the proposed method is more effective than the conventional method. It is expected that this approach will be helpful in evaluating the drug responses of patients by improving the accuracy of image-based cell phenotypic profiling. (C) 2020 Elsevier Ltd. All rights reserved.