A Query-Based Progressive Aggregation Network for 3D Medical Image Segmentation

Accurate 3D medical image segmentation is crucial for knowledge-driven clinical decision-making and computer-aided diagnosis. However, current deep learning methods often fail to effectively integrate local structural details from Convolutional Neural Networks (CNNs) with global semantic context from Transformers due to semantic inconsistency and poor cross-scale feature alignment. To address this, Progressive Query Aggregation Network (PQAN), a novel framework that incorporates knowledge-guided feature interaction mechanisms, is proposed. PQAN employs two complementary query modules: Structural Feature Query, which uses anatomical morphology for boundary-aware representation, and Content Feature Query, which enhances semantic alignment between encoding and decoding stages. To enhance texture perception, a Texture Attention (TA) module based on Sobel operators adds directional edge awareness and fine-detail enhancement. Moreover, a Progressive Aggregation Strategy with Forward and Backward Cross-Stage Attention gradually aligns and refines multi-scale features, thereby reducing semantic deviations during CNN-Transformer fusion. Experiments on public benchmarks demonstrate that PQAN outperforms state-of-the-art models in both global accuracy and boundary segmentation. On the BTCV and FLARE datasets, PQAN had average Dice scores of 0.926 and 0.816, respectively. These results demonstrate PQAN’s ability to capture complex anatomical structures, small targets, and ambiguous organ boundaries, resulting in an interpretable and scalable solution for real-world clinical deployment.