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

TL;DR

This paper introduces PF-DAformer, a domain-adaptive transformer model for accurate proximal femur segmentation in multi-center QCT scans, addressing domain shift issues for reliable bone analysis.

Contribution

It proposes a novel dual-strategy domain adaptation framework combining adversarial and statistical alignment within a transformer-based segmentation model for multi-institutional QCT.

Findings

Achieved robust segmentation across different institutions.

Reduced domain discrepancy effectively with dual alignment strategies.

Validated on large multi-center QCT datasets.

Abstract

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 384 scans from Rochester, Minnesota for proximal femur segmentation. 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.