用于 CT 图像去噪和重建的自监督学习:综述。

IF 3.2 4区 医学 Q2 ENGINEERING, BIOMEDICAL
Biomedical Engineering Letters Pub Date : 2024-09-12 eCollection Date: 2024-11-01 DOI:10.1007/s13534-024-00424-w
Kihwan Choi
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引用次数: 0

摘要

本文综述了用于 CT 图像去噪和重建的自监督学习方法。目前,深度学习已成为医学成像和计算机视觉领域的主流工具。其中,自监督学习方法作为一种在没有干净/噪声参考的情况下学习 CT 图像的技术,引起了人们的极大关注。在简要回顾了 CT 图像去噪和重建的基本原理后,我们探讨了深度学习在 CT 图像去噪和重建中的应用进展。最后,我们重点介绍了用于图像去噪和重建的自监督学习的理论和方法演变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-supervised learning for CT image denoising and reconstruction: a review.

This article reviews the self-supervised learning methods for CT image denoising and reconstruction. Currently, deep learning has become a dominant tool in medical imaging as well as computer vision. In particular, self-supervised learning approaches have attracted great attention as a technique for learning CT images without clean/noisy references. After briefly reviewing the fundamentals of CT image denoising and reconstruction, we examine the progress of deep learning in CT image denoising and reconstruction. Finally, we focus on the theoretical and methodological evolution of self-supervised learning for image denoising and reconstruction.

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来源期刊
Biomedical Engineering Letters
Biomedical Engineering Letters ENGINEERING, BIOMEDICAL-
CiteScore
6.80
自引率
0.00%
发文量
34
期刊介绍: Biomedical Engineering Letters (BMEL) aims to present the innovative experimental science and technological development in the biomedical field as well as clinical application of new development. The article must contain original biomedical engineering content, defined as development, theoretical analysis, and evaluation/validation of a new technique. BMEL publishes the following types of papers: original articles, review articles, editorials, and letters to the editor. All the papers are reviewed in single-blind fashion.
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