MBF-Net: Multi-scale boundary-aware aggregation for bi-directional information exchange and feature reshaping for medical image segmentation

As a critical component of computer-aided diagnosis systems, medical image segmentation plays a vital role in assisting clinicians in making rapid and accurate decisions and formulating treatment plans. Nevertheless, precise medical image segmentation still presents a number of challenges, including insufficient feature extraction capabilities in the presence of limited sample sizes, blurred segmentation boundaries, and information loss between the encoder and decoder. In order to address these issues, we propose a Multi-Scale Boundary-Aware Aggregation Network with Bidirectional Information Exchange and Feature Refinement (MBF-Net) for medical image segmentation. Initially, we design a Multi-Scale Boundary-Aware Aggregation Encoder (MBAE) that aggregates features from different scales and pixel levels within the input images, capturing fine-grained boundary information in deep features and establishing comprehensive global and local multi-scale contextual dependencies. This design significantly enhances the model's understanding of the overall image structure and its ability to discern subtle differences between lesions and background. Subsequently, a Multi-Scale Bidirectional Information Transmission (MBIT) module is introduced, which integrates bidirectional information flow between low-level and high-level features, enabling multi-scale features to flow bidirectionally across different layers. The MBIT module effectively preserves crucial boundary details during cross-layer information transmission, thereby bridging the semantic gap between the encoder and decoder, and thereby improving the clarity of the segmentation boundaries. Finally, we develop a Feature Refinement and Aggregation Fusion (FRAF) module, designed to integrate feature information from various semantic levels , which alleviates discrepancies between features at varying scales, thus enhancing the segmentation accuracy of the network. The generalisation and effectiveness of MBF-Net are validated through comprehensive experiments on a range of tasks, including nuclear segmentation, breast cancer segmentation, polyp segmentation and skin lesion segmentation . Both subjective and objective evaluations demonstrate that MBF-Net significantly outperforms current state-of-the-art methods, achieving average Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) scores of 86.34 % and 78.37 %, respectively. The superior performance of MBF-Net in terms of segmentation accuracy and quality is demonstrated across five public datasets.