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Updated: Jul 16, 2025

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Deep learning-based lung image registration: A review.

Hanguang Xiao¹, Xufeng Xue¹, Mi Zhu¹

¹College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.

Computers in Biology and Medicine

|September 11, 2023

Summary

This summary is machine-generated.

This review surveys deep learning (DL) methods for lung image registration, addressing challenges like soft tissue motion. It classifies DL approaches and analyzes their effectiveness for accurate lung imaging.

Keywords:

Conventional image registration Deep learning Deformable registration Lung image registration

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Lung image registration is crucial for clinical applications but challenged by soft tissue motion.
Existing deep learning (DL) methods for other regions of interest (ROI) are insufficient for lung registration.

Purpose of the Study:

To provide a comprehensive review of DL-based lung image registration methods.
To classify DL approaches and analyze their contributions and limitations.

Main Methods:

Survey of conventional and DL-based lung image registration techniques.
Classification of DL methods by supervision type: fully-supervised, weakly-supervised, and unsupervised.
Statistical analysis of evaluation metrics and loss functions in cited papers.

Main Results:

DL methods show promise but a versatile framework for lung registration is lacking.
Analysis of challenges, limitations, and contributions of various DL approaches.
Summary of publicly available datasets for lung image registration.

Conclusions:

Deep learning offers significant potential for lung image registration.
Further research is needed to overcome current limitations and establish standardized frameworks.
Future trends point towards improved accuracy and broader clinical applicability of DL in lung imaging.