Lightweight multi-scale dynamic selection network for medical image segmentation

Medical image segmentation technology can assist doctors in accurately diagnosing and treating diseases. However, the diversity of segmented targets in scale and shape, as well as the complexity of the background environment, pose challenges for existing segmentation methods. In this context, we propose two effective deep neural networks, the multi -scale dynamic selection network (MDSNet) and its lightweight version MDSNet-Light for medical image segmentation. In MDSNet, we design a multi -scale dynamic selection module to capture multi -scale contextual information and dynamically adjust the acceptance domain of the feature map to achieve feature fusion at the optimal scale. We introduce PixelShuffle and PixelUnshuffle as sampling methods to effectively alleviate information loss issues for small-scale objects caused by pooling. Experimental results show that MDSNet outperforms eight existing superior neural networks in the test data of the publicly available dataset LIDC-IDRI on four evaluation indicators. And MDSNet-Light achieves a good balance between network performance and computational complexity, providing an option for applications in limited computing resource scenarios.