Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105184
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Nursing | - |
dc.creator | Zou, J | en_US |
dc.creator | Gao, B | en_US |
dc.creator | Song, Y | en_US |
dc.creator | Qin, J | en_US |
dc.date.accessioned | 2024-04-12T06:50:40Z | - |
dc.date.available | 2024-04-12T06:50:40Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105184 | - |
dc.language.iso | en | en_US |
dc.publisher | Frontiers Research Foundation | en_US |
dc.rights | © 2022 Zou, Gao, Song and Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in otherforums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | en_US |
dc.rights | The following publication Zou J, Gao B, Song Y and Qin J (2022) A review of deep learning-based deformable medical image registration. Front. Oncol. 12:1047215 is available at https://doi.org/10.3389/fonc.2022.1047215. | en_US |
dc.subject | Clinical applications | en_US |
dc.subject | Computer assisted surgery | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Deformable image registration | en_US |
dc.subject | Medical imaging | en_US |
dc.title | A review of deep learning-based deformable medical image registration | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.doi | 10.3389/fonc.2022.1047215 | en_US |
dcterms.abstract | The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Frontiers in oncology, 2022, v. 12, 1047215 | en_US |
dcterms.isPartOf | Frontiers in oncology | en_US |
dcterms.issued | 2022 | - |
dc.identifier.scopus | 2-s2.0-85144331918 | - |
dc.identifier.eissn | 2234-943X | en_US |
dc.identifier.artn | 1047215 | en_US |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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1_fonc-12-1047215.pdf | 2.96 MB | Adobe PDF | View/Open |
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