Share Email Print
cover

Proceedings Paper

Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database
Author(s): Tong Zheng; Hirohisa Oda; Takayasu Moriya; Takaaki Sugino; Shota Nakamura; Masahiro Oda; Masaki Mori; Hirotsugu Takabatake; Hiroshi Natori; Kensaku Mori
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper newly introduces multi-modality loss function for GAN-based super-resolution that can maintain image structure and intensity on unpaired training dataset of clinical CT and micro CT volumes. Precise non- invasive diagnosis of lung cancer mainly utilizes 3D multidetector computed-tomography (CT) data. On the other hand, we can take μCT images of resected lung specimen in 50 μm or higher resolution. However, μCT scanning cannot be applied to living human imaging. For obtaining highly detailed information such as cancer invasion area from pre-operative clinical CT volumes of lung cancer patients, super-resolution (SR) of clinical CT volumes to μCT level might be one of substitutive solutions. While most SR methods require paired low- and high-resolution images for training, it is infeasible to obtain precisely paired clinical CT and μCT volumes. We aim to propose unpaired SR approaches for clincial CT using micro CT images based on unpaired image translation methods such as CycleGAN or UNIT. Since clinical CT and μCT are very different in structure and intensity, direct appliation of GAN-based unpaired image translation methods in super-resolution tends to generate arbitrary images. Aiming to solve this problem, we propose new loss function called multi-modality loss function to maintain the similarity of input images and corresponding output images in super-resolution task. Experimental results demonstrated that the newly proposed loss function made CycleGAN and UNIT to successfully perform SR of clinical CT images of lung cancer patients into μCT level resolution, while original CycleGAN and UNIT failed in super-resolution.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131305 (10 March 2020); doi: 10.1117/12.2548929
Show Author Affiliations
Tong Zheng, Nagoya Univ. (Japan)
Hirohisa Oda, Nagoya Univ. (Japan)
Takayasu Moriya, Nagoya Univ. (Japan)
Takaaki Sugino, Nagoya Univ. (Japan)
Shota Nakamura, Nagoya Univ. Graduate School of Medicine (Japan)
Masahiro Oda, Nagoya Univ. (Japan)
Masaki Mori, Sapporo-Kosei General Hospital (Japan)
Hirotsugu Takabatake, Sapporo Minami-sanjo Hospital (Japan)
Hiroshi Natori, Keiwakai Nishioka Hospital (Japan)
Kensaku Mori, Nagoya Univ. (Japan)
National Institute of Informatics (Japan)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray