Analysis of the influence of diffeomorphic normalization in the prediction of stable VS progressive MCI conversion with convolutional neural networks

Ramón-Júlvez, Ubaldo (Universidad de Zaragoza) ; Hernández, Mónica (Universidad de Zaragoza) ; Mayordomo, Elvira (Universidad de Zaragoza) ; ADNI

Resumen: We 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%.

Idioma: Inglés

DOI: 10.1109/ISBI45749.2020.9098445

Año: 2020

Publicado en: Proceedings - International Symposium on Biomedical Imaging 2020-April (2020), 1120-1124
ISSN: 1945-7928

Factor impacto SCIMAGO: 0.6 - Radiology, Nuclear Medicine and Imaging - Biomedical Engineering

Financiación: info:eu-repo/grantAgreement/ES/MICINN/TIN2016-80347-R

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)