Insights into traditional Large Deformation Diffeomorphic Metric Mapping and unsupervised deep-learning for diffeomorphic registration and their evaluation

Hernandez, Monica (Universidad de Zaragoza) ; Ramon Julvez, Ubaldo (Universidad de Zaragoza)

Resumen: This 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.

Idioma: Inglés

DOI: 10.1016/j.compbiomed.2024.108761

Año: 2024

Publicado en: Computers in biology and medicine 178 (2024), 108761 [31 pp.]
ISSN: 0010-4825

Financiación: info:eu-repo/grantAgreement/ES/DGA/T64-20R
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2019-104358RB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2022-138703OB-I00

Tipo y forma: Artículo (Versión definitiva)

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

Dataset asociado: Traditional LDDMM vs Deep Learning. Deformation fields on NIREP and OASIS ( https://doi.org/10.21227/d9mb-pn47)