A lightweight segmentation network for endoscopic surgical instruments based on edge refinement and efficient self-attention.

ABSTRACT: In robot-assisted surgical systems, surgical instrument segmentation is a critical task that provides important information for surgeons to make informed decisions and ensure surgical safety. However, current mainstream models often lack precise segmentation edges and suffer from an excess of parameters, rendering their deployment challenging. To address these issues, this article proposes a lightweight semantic segmentation model based on edge refinement and efficient self-attention. The proposed model utilizes a lightweight densely connected network for feature extraction, which is able to extract high-quality semantic information with fewer parameters. The decoder combines a feature pyramid module with an efficient criss-cross self-attention module. This fusion integrates multi-scale da

SUBMITTER: Zhou M 

PROVIDER: S-EPMC10803021 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature