Editorial: Image and geometry analysis for brain informatics
Abstract
Advancements in brain science research have led to the high-throughput generation of vast amounts of data, with the multi-modality, multi-resolution imaging and geometry data being a vital part (1-6). To gain a deeper understanding of the complexities of the brain, it is crucial to properly process and analyze such data. However, due to the overwhelming amount, large scale, and complex nature of the data, these tasks pose significant challenges that need to be overcome through novel and advanced methods. The research articles included in this collection provide up-to-date findings and insights on several issues related to the topic of image and geometry analysis for brain informatics, such as image registration, geometrical model generation, and super-resolution reconstruction.Sun et al. proposed a deep self-calibration-based progressive image registration strategy to address large deformations while avoiding information loss and additional parameters. The method leverages cascaded networks and a novel hierarchical registration strategy to achieve more accurate multi-scale progressive registration and dynamic dataset augmentation. The proposed method was evaluated on optical and MRI image datasets using specific performance criteria and was compared to several state-of-the-art approaches for deformable image registration. The results demonstrated improvements in performance over the existing methods, indicating the effectiveness of the proposed approach.Han et al. provided a…
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