PF-DAformer: Proximal Femur Segmentation via Domain Adaptive Transformer for Dual-Center QCT

Quantitative computed tomography (QCT) plays a crucial role in assessing bone strength and fracture risk by enabling volumetric analysis of bone density distribution in the proximal femur. However, deploying automated segmentation models in practice remains difficult because deep networks trained on one dataset often fail when applied to another. This failure stems from domain shift, where scanners, reconstruction settings, and patient demographics vary across institutions, leading to unstable predictions and unreliable quantitative metrics. Overcoming this barrier is essential for multi-center osteoporosis research and for ensuring that radiomics and structural finite element analysis results remain reproducible across sites. In this work, we developed a domain-adaptive transformer segmentation framework tailored for multi-institutional QCT. Our model is trained and validated on one of the largest hip fracture related research cohorts to date, comprising 1,024 QCT images scans from Tulane University and 398 scans from Mayo Clinic, Rochester, Minnesota for proximal femur segmentation. Importantly, Mayo Clinic, Rochester labels were not used during training; only its unlabeled images were incorporated for domain-invariant feature learning To address domain shift, we integrate two complementary strategies within a 3D TransUNet backbone: adversarial alignment via Gradient Reversal Layer (GRL), which discourages the network from encoding site-specific cues, and statistical alignment via Maximum Mean Discrepancy (MMD), which explicitly reduces distributional mismatches between institutions. This dual mechanism balances invariance and fine-grained alignment, enabling scanner-agnostic feature learning while preserving anatomical detail. Experimental results demonstrate that the combined strategy for domain adaptation using GRL and MMD yields the most consistent performance, achieving a Dice similarity coefficient of 99.53 %, and a Precision of 99.64 %, and a 95^th Percentile Hausdorff Distance (HD95) of 0.77 mm in femur segmentation, all significantly improved over a non-adaptive baseline ( $p<0.01$ ). Beyond surface accuracy, we further show that the radiomic features extracted from adapted segmentation remain virtually identical to the ground truth (Pearson $r>0.99$ , with several > 0.9998), underscoring that fidelity is preserved across domains. Code: https://github.com/MIILab-MTU/PF-DAformer.git.