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Bohai Zhang

Bohai Zhang contributes to research discovery and scholarly infrastructure.

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Published work

2 published item(s)

preprint2026arXiv

MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset

Accurate CT-MRI registration of the cervical spine is essential for preoperative planning because this region is anatomically complex,highly variable,and vulnerable to injury of the vertebral arteries and spinal cord. However,cervical CT-MRI registration remains underexplored,particularly for rigid-deformable hybrid modeling,and the lack of high-quality annotated multimodal data further limits progress. To address these challenges, we construct and release a comprehensively annotated CT-MRI dataset, R-D-Reg, and propose MSR, a rigid-deformable hybrid registration framework for complex joint structures. Specifically, MSR includes a rigid registration module for independent local rigid alignment of individual vertebrae and a deformable registration module with an MSL block that combines Mamba-based global modeling and Swin Transformer-based local modeling through adaptive gating. The rigid and deformable deformation fields are then fused to generate a hybrid field that better preserves local anatomical consistency. The code and dataset are publicly available at https://github.com/ssc1230609-spec/MSR-registration.

preprint2020arXiv

Bayesian Inference of Spatio-Temporal Changes of Arctic Sea Ice

Arctic sea ice extent has drawn increasing interest and alarm from geoscientists, owing to its rapid decline. In this article, we propose a Bayesian spatio-temporal hierarchical statistical model for binary Arctic sea ice data over two decades, where a latent dynamic spatio-temporal Gaussian process is used to model the data-dependence through a logit link function. Our ultimate goal is to perform inference on the dynamic spatial behavior of Arctic sea ice over a period of two decades. Physically motivated covariates are assessed using autologistic diagnostics. Our Bayesian spatio-temporal model shows how parameter uncertainty in such a complex hierarchical model can influence spatio-temporal prediction. The posterior distributions of new summary statistics are proposed to detect the changing patterns of Arctic sea ice over two decades since 1997.