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

Title
Accuracy improvement of quantification information using super-resolution with convolutional neural network for microscopy images
Authors
Kang M.-S.Cha E.Kang E.Ye J.C.Her N.-G.Oh J.-W.Nam D.-H.Kim M.-H.Yang S.
Ewha Authors
김명희
SCOPUS Author ID
김명희scopus
Issue Date
2020
Journal Title
Biomedical Signal Processing and Control
ISSN
1746-8094JCR Link
Citation
Biomedical Signal Processing and Control vol. 58
Keywords
Convolutional neural networkFluorescence microscope imagesImage quantificationSuper-resolution
Publisher
Elsevier Ltd
Indexed
SCIE; SCOPUS scopus
Document Type
Article
Abstract
Background and Objective: Microscope images are used for cell biology and clinical analysis. In general, microscopic images of 10× 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 10× 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 40× high-quality images from originally obtained 10× 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 10× original images and CNN-based 40× images are compared with the ground-truth images. Results: The segmentation accuracy of the CNN-based 40× images is more similar to that of the manual segmenting results than that of the 10× images, as the Sørensen–Dice similarity coefficient. In addition, the CNN-based 40× image results are more similar to those of the manual results than those of the 10× 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. © 2020 Elsevier Ltd
DOI
10.1016/j.bspc.2020.101846
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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