Traditional LDDMM vs Deep Learning. Deformation fields on NIREP and OASIS

Citation Author(s):
Monica
Hernandez
University of Zaragoza
Submitted by:
Monica Hernandez
Last updated:
Tue, 05/07/2024 - 12:58
DOI:
10.21227/d9mb-pn47
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Abstract 

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 understand-
ing 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 bench-
marks 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.

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Funding Agency: 
Ministry of Science, Education, and Universities
Grant Number: 
PID2019-104358RB-I00, PID2022-138703OB-I00