SBC-AL: Structure and Boundary Consistency-Based Active Learning for Medical Image Segmentation

Deep learning-based (DL) models have shown superior representation capabilities in medical image segmentation tasks. However, these representation powers require DL models to be trained by extensive annotated data, but the high annotation costs hinder this, thus limiting their performance. Active learning (AL) is a feasible solution for efficiently training models to demonstrate representation powers under low annotation budgets. It is achieved by querying unlabeled data for new annotations to continuously train models. Thus, the performance of AL methods largely depends on the query strategy. However, designing an efficient query strategy remains challenging due to limited information from unlabeled data for querying. Another challenge is that few methods exploit information in segmentation results for querying. To address them, first, we propose a Structure-aware Feature Prediction (SFP) and Attentional Segmentation Refinement (ASR) module to enable models to generate segmentation results with sufficient information for querying. The incorporation of these modules enhances the models to capture information related to the anatomical structures and boundaries. Additionally, we propose an uncertainty-based querying strategy to leverage information in segmentation results. Specifically, uncertainty is evaluated by assessing the consistency of anatomical structure and boundary information within segmentation results by calculating Structure Consistency Score (SCS) and Boundary Consistency Score (BCS). Subsequently, data is queried for annotations based on uncertainty. The incorporation of SFP and ASR-enhanced segmentation models and this uncertainty-based querying strategy into a standard AL strategy leads to a novel method, termed Structure and Boundary Consistency-based Active Learning (SBC-AL). Experimental evaluations conducted on the ACDC dataset and KiTS19 dataset demonstrate the superior performance of SBC-AL on efficient model training under low annotation budgets over other AL methods. Our code is available at https://github.com/Tmin16/SBC-AL .