000131293 001__ 131293
000131293 005__ 20240208144956.0
000131293 0247_ $$2doi$$a10.1109/ISBI45749.2020.9098445
000131293 0248_ $$2sideral$$a118591
000131293 037__ $$aART-2020-118591
000131293 041__ $$aeng
000131293 100__ $$aRamón-Júlvez, Ubaldo$$uUniversidad de Zaragoza
000131293 245__ $$aAnalysis of the influence of diffeomorphic normalization in the prediction of stable VS progressive MCI conversion with convolutional neural networks
000131293 260__ $$c2020
000131293 5060_ $$aAccess copy available to the general public$$fUnrestricted
000131293 5203_ $$aWe study the effect of the selection of diffeomorphic normalization in the performance of Spasov''s deep-learning system for the problem of progressive MCI vs stable MCI discrimination. We considered different degrees of normalization (no, affine and non-rigid normalization) and two diffeomorphic registration methods (ANTS and BL PDE-LDDMM) with different image similarity metrics (SSD, NCC, and lNCC) yielding qualitatively different deformation models and quantitatively different degrees of registration accuracy. BL PDE-LDDMM NCC achieved the best performing accuracy with median values of 89%. Surprisingly, the accuracy of no and affine normalization was also among the highest, indicating that the deep-learning system is powerful enough to learn accurate models for pMCI vs sMCI discrimination without the need for normalization. However, the best sensitivity values were obtained by BL PDE-LDDMM SSD and NCC with median values of 97% and 94% while the sensitivity of the remaining methods stayed under 88%.
000131293 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/TIN2016-80347-R
000131293 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000131293 592__ $$a0.6$$b2020
000131293 593__ $$aRadiology, Nuclear Medicine and Imaging$$c2020
000131293 593__ $$aBiomedical Engineering$$c2020
000131293 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000131293 700__ $$0(orcid)0000-0003-1270-5852$$aHernández, Mónica$$uUniversidad de Zaragoza
000131293 700__ $$0(orcid)0000-0002-9109-5337$$aMayordomo, Elvira$$uUniversidad de Zaragoza
000131293 700__ $$aADNI
000131293 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000131293 773__ $$g2020-April (2020), 1120-1124$$pProc. (IEEE Int. Symp. Biomed. Imaging)$$tProceedings - International Symposium on Biomedical Imaging$$x1945-7928
000131293 8564_ $$s528920$$uhttps://zaguan.unizar.es/record/131293/files/texto_completo.pdf$$yPostprint
000131293 8564_ $$s2951764$$uhttps://zaguan.unizar.es/record/131293/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000131293 909CO $$ooai:zaguan.unizar.es:131293$$particulos$$pdriver
000131293 951__ $$a2024-02-08-14:47:20
000131293 980__ $$aARTICLE