Learning generalizable features for bone fracture segmentation using limited annotations

Abstract

Accurate automated segmentation of bone fractures from computed tomography (CT) requires large amounts of annotated data to train deep learning models. However, obtaining such annotations presents unique challenges, as the process demands expert knowledge to identify diverse fracture patterns, assess severity, and account for individual anatomical variations. This makes the annotation process highly time-consuming and expensive. Although semi-supervised learning methods can utilize unlabeled data, existing approaches often struggle with the complexity and variability of fracture morphologies, as well as limited generalizability across datasets. To address these challenges, we propose an effective training strategy based on masked autoencoder (MAE) for accurate bone fracture segmentation in CT. The proposed method combines MAE-based anatomical structure learning from unlabeled data with cross-scale cascaded attention (CSCA)-enhanced fine-tuning that focuses on fracture-specific detail preservation. The method is evaluated on two CT datasets: 180 tibial plateau fractures (TPF) and 103 hip fractures (HF). It outperforms representative semi-supervised baselines, achieving high segmentation accuracy with only 20 annotated cases and demonstrating strong cross-site generalization. These results demonstrate that the proposed training strategy effectively improves the efficiency and accuracy of bone fracture segmentation while maintaining strong generalizability across anatomically distinct datasets. This makes the method well-suited for real-world clinical deployment, particularly in data-scarce or resource-constrained environments. The code is publicly available at https://github.com/yuepeiyan/GeneralizableFractureSeg.