Reconstruction from multi-planar MRI with foundation models for uterine fibroid analysis

Uterine fibroids represent one of the most common gynecological diseases and accurate 3D reconstruction is a prerequisite for clinical diagnosis, surgical planning, and treatment evaluation. Routine clinical protocols typically involve a set of orthogonal sagittal, coronal, and transverse MRI scans to assess morphology. These images usually have anisotropic voxels with sparse 3D coverage and existing assessment schemes are often limited to different 2D views based on planar segmentation. Moreover, models trained on data from individual centers often fail to generalize to wider datasets. Accurate and consistent 3D reconstruction for quantitative uterine fibroid analysis, particularly with unsupervised domain adaptation (UDA), is an unmet clinical need. This paper proposes the Foundation Model-Guided Adaptive Segmentation (FGAS) framework for automatic annotation-free multi-planar uterine fibroid MRI segmentation. FGAS uses anatomical priors for pseudo-label optimization, and integrates multi-view consistency constraints and connected component control to reduce planar dependency and suppress false positives. Extensive experiments on clinical datasets demonstrate the superior performance of FGAS, improving the Dice similarity coefficient from 42.8% of baseline models to 70.6%, outperforming existing state-of-the-art UDA and multi-plane methods. These results indicate that FGAS can achieve robust, high-accuracy automated reconstruction for annotation-free multi-view and cross-domain image analysis.