Multi-view Vision Mamba U-Shaped Network Framework for Medical Image Segmentation.

Abstract

Medical image segmentation requires computational methods that accurately capture global context, local boundaries, and multiscale anatomical structures while remaining reproducible across different applications. This article presents a protocol for constructing, training, and evaluating a Multi-view Vision Mamba U-Shaped Network framework for two-dimensional medical image segmentation. The protocol provides a reproducible workflow that includes public dataset acquisition, image and mask preprocessing, network construction, model training, checkpoint selection, and quantitative and qualitative performance evaluation. The framework incorporates multiview feature scanning to capture complementary spatial, contour, scale, and boundary information and applies multistage feature fusion within a U-shaped encoder-decoder architecture to improve feature integration during segmentation. The protocol is demonstrated using publicly available skin lesion and abdominal organ segmentation datasets. Under the described implementation workflow, the framework achieves competitive segmentation performance using standard evaluation metrics. By following the procedures presented in this protocol, researchers can reproduce the model implementation, train the network using defined experimental settings, evaluate segmentation performance, and adapt the workflow for related medical image segmentation tasks requiring reproducible deep learning-based analysis.