Accurate electrode instance identification from X-ray images is essential for reliable overhang analysis in lithium-ion battery electrode inspection. However, industrial X-ray radiographs often exhibit blurred edges, contrast attenuation, and structural occlusions caused by layer overlap, leading to discontinuous or distorted segmentation masks requiring post refinement. Existing mask refinement methods struggle in such low-contrast and densely packed patterns because they mostly rely on data-driven training without explicit physical knowledge. This paper presents a Bayesian refinement method that corrects coarse electrode masks through probabilistic inference with context-aware geometric priors. The proposed model integrates curvature, lattice, and global assembly priors with the image-based likelihood to derive a maximum a posteriori formulation, while an additional language-conditioned hyperprior estimated by an LLM adaptively modulates the strength of each prior according to morphological context. The core contribution is the interpretable geometric-prior Bayesian framework, which remains the sole optimization engine and accounts for most of the accuracy gain; the LLM is an enhancement module that only supplies per-image hyperparameter guidance and neither drives the optimization nor generates any mask. This design enables training-free, interpretable, and physically consistent restoration of electrode boundaries across diverse imaging conditions. Experiments on real X-ray datasets of cylindrical cells demonstrate that the proposed approach significantly improves the mask quality especially boundary accuracy and continuity compared with existing refinement methods in practical battery inspection. Beyond segmentation fidelity, the refined masks improve measurement-grade overhang analysis, reducing the standard deviation of electrode height and spacing by approximately 18% and 22%, respectively.
