RDLR: A Robust Deep Learning-Based Image Registration Method for Pediatric Retinal Images.
Retinal diseases stand as a primary cause of childhood blindness. Analyzing the progression of these diseases requires close attention to lesion morphology and spatial information. Standard image registration methods fail to accurately reconstruct pediatric fundus images containing significant disto...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 6; pp. 3131 - 3146 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283977&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283977 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283977 182283977 182283977 10.1007/s10278-024-01154-2 182283977 ppf: 3131 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: RDLR: A Robust Deep Learning-Based Image Registration Method for Pediatric Retinal Images. aug: au: Zhou, Hao Yang, Wenhan Sun, Limei Huang, Li Li, Songshan Luo, Xiaoling Jin, Yili Sun, Wei Yan, Wenjia Li, Jing Ding, Xiaoyan He, Yao Xie, Zhi affil: State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China sug: subj: Retinal Diseases Radiography Retinal Diseases Pathology Image Interpretation, Computer Assisted Methods Image Processing, Computer Assisted Methods Deep Learning Human Male Female Child Child, Preschool Infant Quality Control (Technology) Reliability Child: 6-12 years Child, Preschool: 2-5 years Infant: 1-23 months Male Female ab: Retinal diseases stand as a primary cause of childhood blindness. Analyzing the progression of these diseases requires close attention to lesion morphology and spatial information. Standard image registration methods fail to accurately reconstruct pediatric fundus images containing significant distortion and blurring. To address this challenge, we proposed a robust deep learning–based image registration method (RDLR). The method consisted of two modules: registration module (RM) and panoramic view module (PVM). RM effectively integrated global and local feature information and learned prior information related to the orientation of images. PVM was capable of reconstructing spatial information in panoramic images. Furthermore, as the registration model was trained on over 280,000 pediatric fundus images, we introduced a registration annotation automatic generation process coupled with a quality control module to ensure the reliability of training data. We compared the performance of RDLR to the other methods, including conventional registration pipeline (CRP), voxel morph (WM), generalizable image matcher (GIM), and self-supervised techniques (SS). RDLR achieved significantly higher registration accuracy (average Dice score of 0.948) than the other methods (ranging from 0.491 to 0.802). The resulting panoramic retinal maps reconstructed by RDLR also demonstrated substantially higher fidelity (average Dice score of 0.960) compared to the other methods (ranging from 0.720 to 0.783). Overall, the proposed method addressed key challenges in pediatric retinal imaging, providing an effective solution to enhance disease diagnosis. Our source code is available at https://github.com/wuwusky/RobustDeepLeraningRegistration. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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