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

This dataset acompanies our article titled "Insights into traditional Large Deformation Diffeomorphic Metric Mapping and unsupervised deep-learning for diffeomorphic registration and their evaluation", Computers in Biology and Medicine, 2024. 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.

本数据集伴随我们的文章《对传统大变形流形测地映射方法与无监督深度学习在非刚性配准中的应用及其评估的洞察》，发表于《生物医学计算机》杂志，2024年。该论文探讨了传统大变形流形测地映射方法与无监督深度学习在非刚性配准中的应用之间的联系，尤其强调了流形配准的重要性。研究提供了有益的见解，并建立了方法之间的联系，从而有助于深刻理解方法论的全貌。本研究中考虑的方法在T1w MRI图像中进行了广泛的评估，使用传统的NIREP和Learn2Reg OASIS评估协议，重点关注公平性，以建立公平的基准并促进有针对性的比较。通过对结果的全面分析，我们解答了关键问题，包括性能中准确性与变换质量之间的复杂关系、配准成分对性能影响的无歧义分析，以及基准方法和基线的确定。我们提供了对传统方法和深度学习方法之优缺点的重要洞察，揭示了它们的比较性能，并指导了该领域的未来进步。