Regular geometric targets under microscopic scenes, such as microspheres, micropores, and microtubes, are characterized by small scales, low contrast, and degraded boundaries. Masks generated by general segmentation methods often fail to directly support high-precision geometric parameter measurement. This paper proposes a mask optimization method for the high-precision extraction of regular geometric features in microscopic scenes. We establish a mask optimization framework that integrates initial mask generation with geometric consistency refinement. Mask initialization is first performed through segmentation and adaptive super-resolution (SR) under low annotation constraints. Subsequently, an iterative optimization strategy that fuses multi-dimensional pixel features with regular geometric priors is designed. By incorporating geometric features extracted from the current mask while maintaining stable pixel-level observations, the mask is progressively corrected until convergence to generate target masks with continuous boundaries that satisfy stringent geometric constraints. Our experimental results on a sphere–tube assembly dataset demonstrate that the proposed method achieves lower geometric errors on successfully fitted samples and significantly improves the fitting success rate. Ablation studies further confirm the critical roles of dynamic SR and iterative mask optimization in enhancing overall precision and stability. These findings suggest that for microscopic regular geometric measurement tasks, integrating geometric-consistency constraints into mask optimization effectively improves both the accuracy and robustness of geometric feature extraction.
Kang et al. (2026) studied this question.