Coarse-to-fine cascade framework for cross-modality super-resolution on clinical/micro CT dataset

Tong Zheng; Hirohisa Oda; Yuichiro Hayashi; Shota Nakamura; Masaki Mori; Hirotsugu Takabatake; Hiroshi Natori; Masahiro Oda; Kensaku Mori

doi:10.1117/12.2611311

4 April 2022 Coarse-to-fine cascade framework for cross-modality super-resolution on clinical/micro CT dataset

Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Shota Nakamura, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Masahiro Oda, Kensaku Mori

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120321T (2022) https://doi.org/10.1117/12.2611311
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

This paper proposes a super-resolution (SR) method, for performing SR of medical images training on a newly-built lung clinical CT / micro CT dataset. Conventional SR methods are always trained on bicubic downsampled images (LR) / original images (HR) image pairs. However, registration precision between LR and HR images is not satisfying for SR. Low precision of registration results in conventional SR methods’ unsatisfactory performance in medical imaging. We propose a coarse-to-fine cascade framework for performing SR of medical images. First, we design a coarse SR network to translate LR medical images into coarse SR images. Next, we utilize a fully convolutional network (FCN) to perform fine SR (translate coarse SR images to fine SR images). We conducted experiments using a newly-built clinical / micro CT lung specimen dataset. Experimental results illustrated that our method obtained PSNR of 27.30 and SSIM of 0.75, outperforming conventional method’s PSNR 19.08 and SSIM 0.63.

Conference Presentation

Citation Download Citation

Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Shota Nakamura, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Masahiro Oda, and Kensaku Mori "Coarse-to-fine cascade framework for cross-modality super-resolution on clinical/micro CT dataset", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120321T (4 April 2022); https://doi.org/10.1117/12.2611311

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