DLKUNet : A Lightweight and Efficient Network With Depthwise Large Kernel for Medical Image Segmentation
Junan Zhu, Zhizhe Tang, Ping Ma, Zheng Liang, Chuanjian Wang · International Journal of Imaging Systems and Technology · 2025
ABSTRACT Accurate multi‐organ segmentation is crucial in computer‐aided diagnosis, surgical navigation, and radiotherapy. Deep learning‐based methods for automated multi‐organ segmentation have made significant progress recently. However, these improvements often increase model complexity, leading to higher computational costs. To address this problem, we propose a lightweight and efficient network with depthwise large kernel, called DLKUNet. Firstly, we utilize a hierarchical architecture with large kernel convolution to effectively capture multi‐scale features. Secondly, we constructed three segmentation models with different layers to meet different speed and accuracy requirements. Additionally, we employ a novel training strategy that works seamlessly with this module to enhance performance. Finally, we conducted extensive experiments on the multi‐organ abdominal segmentation (Synapse) and the Automated Cardiac Diagnosis Challenge (ACDC) dataset. DLKUNet‐L significantly improves the 95% Hausdorff Distance to 13.89 mm with 65% parameters of Swin‐Unet on the Synapse. Furthermore, DLKUNet‐S and DLKUNet‐M use only 4.5% and 16.52% parameters of Swin‐Unet, achieving Dice Similarity Coefficient 91.71% and 91.74% on the ACDC. These results underscore the proposed model's superior performance in terms of accuracy, efficiency, and practical applicability.