000136113 001__ 136113
000136113 005__ 20240711103552.0
000136113 0247_ $$2doi$$a10.1016/j.compbiomed.2024.108761
000136113 0248_ $$2sideral$$a139045
000136113 037__ $$aART-2024-139045
000136113 041__ $$aeng
000136113 100__ $$0(orcid)0000-0003-1270-5852$$aHernandez, Monica$$uUniversidad de Zaragoza
000136113 245__ $$aInsights into traditional Large Deformation Diffeomorphic Metric Mapping and unsupervised deep-learning for diffeomorphic registration and their evaluation
000136113 260__ $$c2024
000136113 5060_ $$aAccess copy available to the general public$$fUnrestricted
000136113 5203_ $$aThis paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. The study provides useful insights and establishes connections between the methods, thereby facilitating a profound understanding of the methodological landscape. The methods considered in our study are extensively evaluated in T1w MRI images using traditional NIREP and Learn2Reg OASIS evaluation protocols with a focus on fairness, to establish equitable benchmarks and facilitate informed comparisons. Through a comprehensive analysis of the results, we address key questions, including the intricate relationship between accuracy and transformation quality in performance, the disentanglement of the influence of registration ingredients on performance, and the determination of benchmark methods and baselines. We offer valuable insights into the strengths and limitations of both traditional and deep-learning methods, shedding light on their comparative performance and guiding future advancements in the field.
000136113 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T64-20R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-104358RB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-138703OB-I00
000136113 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000136113 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000136113 700__ $$aRamon Julvez, Ubaldo$$uUniversidad de Zaragoza
000136113 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000136113 773__ $$g178 (2024), 108761 [31 pp.]$$pComput. biol. med.$$tComputers in biology and medicine$$x0010-4825
000136113 787__ $$tTraditional LDDMM vs Deep Learning. Deformation fields on NIREP and OASIS$$whttps://doi.org/10.21227/d9mb-pn47
000136113 8564_ $$s33559516$$uhttps://zaguan.unizar.es/record/136113/files/texto_completo.pdf$$yVersión publicada
000136113 8564_ $$s2499781$$uhttps://zaguan.unizar.es/record/136113/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000136113 909CO $$ooai:zaguan.unizar.es:136113$$particulos$$pdriver
000136113 951__ $$a2024-07-11-08:38:34
000136113 980__ $$aARTICLE