Enhancing vision Mamba with two-dimensional position embedding and multiscale fusion for medical image segmentation.

Background: Medical image segmentation plays a critical role in diagnostic and scientific research, enabling the delineation of regions of interest and the identification of fine-grained lesion at the pixel level. Recent advances in deep learning, particularly in Vision Transformers (ViTs), have significantly accelerated this field. However, existing ViT models often struggle to capture long-range dependencies while maintaining computational efficiency. Therefore, we aimed to design a model that can not only accurately identify target lesions but also ensure computational efficiency. Methods: To achieve this goal, we established vision Mamba, an efficient visual state space model (SSM), and proposed enhancements through the integration of two-dimensional (2D) position embedding and a multiscale feature fusion block (MB). Our 2D position embedding method effectively incorporates spatial information into patch embedding, while the MB supplements detailed information lost during Mamba's sequential processing. Results: Experiments on three public datasets showed the superiority of our approach. Compared to a suboptimal model, our proposed method achieved average performance improvements of 1.29%, 1.18%, 5.52%, 1.18%, and 1.03% across the three datasets in terms of the dice similarity coefficient, volumetric overlap error, average surface distance between objects, Jaccard coefficient, and recall, respectively. Conclusions: Our method not only enhances segmentation accuracy but also retains computational efficiency, making it a promising candidate for practical medical image segmentation applications.