{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["95(1129)"],"submitter":["Chi W"],"pubmed_abstract":["<h4>Objectives</h4>To develop a rapid and accurate 4D deformable image registration (DIR) approach for online adaptive radiotherapy.<h4>Methods</h4>We propose a deep learning (DL)-based few-shot registration network (FR-Net) to generate deformation vector fields from each respiratory phase to an implicit reference image, thereby mitigating the bias introduced by the selection of reference images. The proposed FR-Net is pretrained with limited unlabeled 4D data and further optimized by maximizing the intensity similarity of one specific four-dimensional computed tomography (4DCT) scan. Because of the learning ability of DL models, the few-shot learning strategy facilitates the generalization of the model to other 4D data sets and the acceleration of the optimization process.<h4>Results</h4>"],"journal":["The British journal of radiology"],"pagination":["20210819"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8722248"],"repository":["biostudies-literature"],"pubmed_title":["Few-shot learning for deformable image registration in 4DCT images."],"pmcid":["PMC8722248"],"pubmed_authors":["Xiang Z","Guo F","Chi W"],"additional_accession":[]},"is_claimable":false,"name":"Few-shot learning for deformable image registration in 4DCT images.","description":"<h4>Objectives</h4>To develop a rapid and accurate 4D deformable image registration (DIR) approach for online adaptive radiotherapy.<h4>Methods</h4>We propose a deep learning (DL)-based few-shot registration network (FR-Net) to generate deformation vector fields from each respiratory phase to an implicit reference image, thereby mitigating the bias introduced by the selection of reference images. The proposed FR-Net is pretrained with limited unlabeled 4D data and further optimized by maximizing the intensity similarity of one specific four-dimensional computed tomography (4DCT) scan. Because of the learning ability of DL models, the few-shot learning strategy facilitates the generalization of the model to other 4D data sets and the acceleration of the optimization process.<h4>Results</h4>","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2025-04-21T15:22:34.052Z","creation":"2025-04-21T15:22:34.052Z"},"accession":"S-EPMC8722248","cross_references":{"pubmed":["34662242"],"doi":["10.1259/bjr.20210819"]}}