RSSNet: A Fine-tuned Deep Learning Network for Robotic Surgical-tool Segmentation

Robotic systems have significantly transformed modern surgical practice by improving surgical precision and minimizing invasiveness. Crucial to these systems is the accurate segmentation of surgical tools, which remains a challenging task due to the complex surgical scenarios and the diversity of tools used. This paper presents a novel deep learning architecture, the Robotic Surgical Tool Segmentation Network (RSSNet), designed to enhance the accuracy of surgical tool segmentation in robotic surgeries. The proposed RSSNet model combines the power of Atrous Spatial Pyramid Pooling (ASPP) with the efficiency of average pooling layers to extract and encode intricate details for accurate segmentation. A fine-tuning strategy on the average pooling layers further enhances the model’s performance, particularly for small and intricate details. The model was rigorously tested on two primary datasets, Kvasir and EndoVis 2017, along with additional surgical datasets, demonstrating robustness and versatility. In particular, RSSNet outperformed state-of-the-art models in all comparisons. An ablation study further confirmed the effectiveness of each component in the model architecture, particularly the fine-tuned average pooling layers and the ASPP module. The study offers a novel, efficient, and high-performing solution for surgical tool segmentation, an essential step toward fully autonomous robotic surgeries. With a performance score of 96.78%, this work provides a baseline for optimizing fine-tuning strategies and incorporating additional data modalities for enhanced performance.