SwinD-Net: a lightweight segmentation network for laparoscopic liver segmentation

Abstract

The real-time requirement for image segmentation in laparoscopic surgical assistance systems is extremely high. Although traditional deep learning models can ensure high segmentation accuracy, they suffer from a large computational burden. In the practical setting of most hospitals, where powerful computing resources are lacking, these models cannot meet the real-time computational demands. We propose a novel network SwinD-Net based on Skip connections, incorporating Depthwise separable convolutions and Swin Transformer Blocks. To reduce computational overhead, we eliminate the skip connection in the first layer and reduce the number of channels in shallow feature maps. Additionally, we introduce Swin Transformer Blocks, which have a larger computational and parameter footprint, to extract global information and capture high-level semantic features. Through these modifications, our network achieves desirable performance while maintaining a lightweight design. We conduct experiments on the CholecSeg8k dataset to validate the effectiveness of our approach. Compared to other models, our approach achieves high accuracy while significantly reducing computational and parameter overhead. Specifically, our model requires only 98.82 M floating-point operations (FLOPs) and 0.52 M parameters, with an inference time of 47.49 ms per image on a CPU. Compared to the recently proposed lightweight segmentation network UNeXt, our model not only outperforms it in terms of the Dice metric but also has only 1/3 of the parameters and 1/22 of the FLOPs. In addition, our model achieves a 2.4 times faster inference speed than UNeXt, demonstrating comprehensive improvements in both accuracy and speed. Our model effectively reduces parameter count and computational complexity, improving the inference speed while maintaining comparable accuracy. The source code will be available at https://github.com/ouyangshuiming/SwinDNet.

Keywords:

• Image segmentation
• lightweight model
• deep learning
• laparoscopic liver surgery

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China (No. 62172401 and 82227806), the Education Science Planning Project of Guangdong Provincial Department of Education (Higher Education Special Project) ‘Research on the Evaluation Index System of Information Literacy Course Teaching Quality in Applied Undergraduate Universities’ (No. 2022GXJK325), the Guangdong Natural Science Foundation (Nos. 2022A1515010439, 2022A0505020019, and 2023A1515012587), the Shenzhen Key Basic Research Grant (No. JCYJ20220818101802005), and the Zhuhai Science and Technology Program (ZH22017002210017PWC), the Shenzhen Basic Research Grant (No. JCYJ20220531100614032).