Super-resolution reconstruction of medical image via depth residual network

Jinrong Ding; Yefeng Shu; Jiasong Sun; Chao Zuo; Qian Chen

doi:10.1117/12.2642021

30 December 2022 Super-resolution reconstruction of medical image via depth residual network

Jinrong Ding, Yefeng Shu, Jiasong Sun, Chao Zuo, Qian Chen

Proceedings Volume 12316, Advanced Optical Imaging Technologies V; 1231607 (2022) https://doi.org/10.1117/12.2642021
Event: SPIE/COS Photonics Asia, 2022, Online Only

Conference Poster

Abstract

Recently, whole slide imaging (WSI) has become the gold standard for the clinical diagnosis of various diseases. It not only strengthens the cooperation between the computer and pathologists, but also promotes the development of remote primary diagnosis. WSI usually uses 20x and 40x objective lens scanners to scan the slides. Compared with the low-magnification lens, the high-magnification lens could provide high-resolution (HR) images, while sacrificing the large field-of-view (FOV), large depth-of-field (DOF), and high scanning efficiency instead. In this paper, we propose an image super-resolution (SR) reconstruction method based on a deep residual network (DSN), which improves flexibility and recovers the effective information neglected by the conventional network. After experimental verification, our method achieves large FOV, high scanning efficiency, and HR images simultaneously for assisting the diagnosis of pathologists.

Citation Download Citation

Jinrong Ding, Yefeng Shu, Jiasong Sun, Chao Zuo, and Qian Chen "Super-resolution reconstruction of medical image via depth residual network", Proc. SPIE 12316, Advanced Optical Imaging Technologies V, 1231607 (30 December 2022); https://doi.org/10.1117/12.2642021

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