MSLUnet: A Medical Image Segmentation Network Incorporating Multi-Scale Semantics and Large Kernel Convolution

Abstract

In recent years, various deep-learning methodologies have been developed for processing medical images, with Unet and its derivatives proving particularly effective in medical image segmentation. Our primary objective is to enhance the accuracy of these networks while also reducing the number of parameters and computational demands to facilitate deployment on mobile medical devices. To this end, we introduce a novel medical image segmentation network, MSLUnet, which aims to minimize parameter count and computational load without compromising segmentation effectiveness. The network features a U-shaped architecture. In the encoder module, we utilize multiple small convolutional kernels for successive convolutions rather than large ones, allowing for capturing multi-scale feature information at granular levels through varied receptive field scales. In the decoder module, an inverse bottleneck structure with depth-separable convolution employing large kernels is incorporated. This design effectively extracts spatial dimensional information and ensures a comprehensive integration of both shallow and deep features. Additionally, a lightweight three-branch attention mechanism within the skip connections enhances information transfer by capturing global contextual data across spatial and channel dimensions. Experimental evaluations conducted on several publicly available medical image datasets indicate that MSLUnet is more competitive than existing models in terms of efficiency and effectiveness.