Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower Extremities

Purpose: Obtaining manual annotations to train deep learning (DL) models for auto-segmentation is often time-consuming. Uncertainty-based Bayesian active learning (BAL) is a widely-adopted method to reduce annotation efforts. Based on BAL, this study introduces a hybrid representation-enhanced sampling strategy that integrates density and diversity criteria to save manual annotation costs by efficiently selecting the most informative samples. Methods: The experiments are performed on two lower extremity (LE) datasets of MRI and CT images by a BAL framework based on Bayesian U-net. Our method selects uncertain samples with high density and diversity for manual revision, optimizing for maximal similarity to unlabeled instances and minimal similarity to existing training data. We assess the accuracy and efficiency using Dice and a proposed metric called reduced annotation cost (RAC), respectively. We further evaluate the impact of various acquisition rules on BAL performance and design an ablation study for effectiveness estimation. Results: The proposed method showed superiority or non-inferiority to other methods on both datasets across two acquisition rules, and quantitative results reveal the pros and cons of the acquisition rules. Our ablation study in volume-wise acquisition shows that the combination of density and diversity criteria outperforms solely using either of them in musculoskeletal segmentation. Conclusion: Our sampling method is proven efficient in reducing annotation costs in image segmentation tasks. The combination of the proposed method and our BAL framework provides a semi-automatic way for efficient annotation of medical image datasets.