0 259

Cited 8 times in

Self-supervised inter- and intra-slice correlation learning for low-dose CT image restoration without ground truth

Authors
 Kihwan Choi  ;  Joon Seok Lim  ;  Sungwon Kim 
Citation
 EXPERT SYSTEMS WITH APPLICATIONS, Vol.209 : 118072, 2022-12 
Journal Title
EXPERT SYSTEMS WITH APPLICATIONS
ISSN
 0957-4174 
Issue Date
2022-12
Keywords
Low-dose CT ; Image denoising ; Self-supervised learning ; Intra-slice correlation - Inter-slice correlation - Online finetuning
Abstract
Training a convolutional neural network (CNN) to reduce noise in low-dose CT (LDCT) images typically relies on supervised learning, which requires input–target pairs of noisy LDCT and corresponding full-dose CT (FDCT) images. Although previous approaches have shown promising results in LDCT image denoising, it is difficult to acquire clinical datasets of LDCT-FDCT image pairs, which require additional and unnecessary radiation dose delivery to patients. In this paper, we propose a self-supervised learning approach to training a CNN-based denoiser with LDCT images alone. As a means of self-supervision, the proposed approach searches inter-pixel correlation of LDCT images in
-direction as well as in-plane direction. To regularize the CNN-based denoiser, thicker LDCT slices are used as image priors during the self-supervised training process in our approach. For efficient self-supervised learning, we adopt a two-stage training strategy with offline pretraining and online finetuning. The proposed approach is thoroughly evaluated with public and private clinical LDCT datasets. Both image quality measures and clinical assessments indicate that the self-supervised denoising model simultaneously reduces noise level and restores anatomical information in LDCT images from the images alone. The experimental results also show that our online finetuning scheme can improve the denoising performance of supervised learning models as well as self-supervised learning models at test time.
Full Text
https://www.sciencedirect.com/science/article/pii/S0957417422012751
DOI
10.1016/j.eswa.2022.118072
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sungwon(김성원) ORCID logo https://orcid.org/0000-0001-5455-6926
Lim, Joon Seok(임준석) ORCID logo https://orcid.org/0000-0002-0334-5042
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/193177
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links