中文版 | English
Title

A Survey on Deep Learning-Based Diffeomorphic Mapping

Author
Corresponding AuthorTang, Xiaoying
Publication Years
2023
ISBN
9783030986605 ; 9783030986612
Source Title
Publication Place
Berlin, Germany
Publisher
Pages
1289-1321
Abstract
Diffeomorphic mapping is a specifific type of registration methods that can be used to align biomedical structures for subsequent analyses. Diffeomorphism not only provides a smooth transformation that is desirable between a pair of biomedical template and target structures but also offers a set of statistical metrics that can be used to quantify characteristics of the pair of structures of interest. However, traditional one-to-one numerical optimization is time-consuming, especially for 3D images of large volumes and 3D meshes of numerous vertices. To address this computationally expensive problem while still holding desirable properties, deep learning-based diffeomorphic mapping has been extensively explored, which learns a mapping function to perform registration in an end- to-end fashion with high computational effificiency on GPU. Learning-based approaches can be categorized into two types, namely, unsupervised and super- vised. In this chapter, recent progresses on these two major categories will be covered. We will review the general frameworks of diffeomorphic mapping as well as the loss functions, regularizations, and network architectures of deep learning-based diffeomorphic mapping. Specififically, unsupervised ones can be further subdivided into convolutional neural network (CNN)-based methods and variational autoencoder-based methods, according to the network architectures, the corresponding loss functions, as well as the optimization strategies, while supervised ones mostly employ CNN. After summarizing recent achievements and challenges, we will also provide an outlook of future directions to fully exploit deep learning-based diffeomorphic mapping and its potential roles in biomedical applications such as segmentation, detection, and diagnosis.
DOIhttps://doi.org/10.1007/978-3-030-98661-2_108
SUSTech Authorship
First ; Corresponding
Data Source
人工提交
Publication Status
正式出版
Citation statistics
Cited Times [WOS]:0
Document TypeBook chapter
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/527603
DepartmentDepartment of Electrical and Electronic Engineering
Affiliation
1.Southern University of Science and Technology ,Shenzhen, Guangdong, China
2.The University of British Columbia,Vancouver, BC, Canada
First Author AffilicationSouthern University of Science and Technology
Corresponding Author AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Yang, Huilin,Lyu, Junyan,Tam, Roger,et al. A Survey on Deep Learning-Based Diffeomorphic Mapping. Berlin, Germany:Springer International Publishing,2023:1289-1321.
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