Traditional LDDMM vs Deep Learning deformation fields on NIREP and OASIS

This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods andunsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. Thestudy 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 imagesusing 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 theinfluence of registration ingredients on performance, and the determination of benchmark methods and baselines. We offervaluable insights into the strengths and limitations of both traditional and deep-learning methods, shedding light on theircomparative performance and guiding future advancements in the field.