Intelligent Inspection Guidance of Urethral Endoscopy Based on SLAM with Blood Vessel Attentional Features

Abstract

Due to small imaging range of lens, blurring by jitter in the operation process and high similarity of urethral image features observed in different positions, doctors often face challenges in conducting a quick and comprehensive microscopic examination. In this paper, we combine image processing, simultaneous localization and mapping (SLAM) and intelligent navigation technologies to build an ORB-SLAM-based auxiliary microscopy guiding system. It can automatically process real-time microscopy videos, analyze the doctor’s detection path and provide direction for areas that have not been detected, assisting the doctor in completing urethral wall detection. In this system, a generative adversarial network-based deblurring algorithm is used to deblur the urethral images before SLAM processing. We creatively propose a vascular attention-based feature extraction algorithm tailored for urethral images. This algorithm combines F3Net and U-Net networks to detect the main body and branch points of blood vessels, respectively, which demonstrates the capability to assist the SLAM system in tracking the urethra more stably. Moreover, we design the direction guidance rules to aid doctors in urethral endoscopy. The system has been evaluated with a real urethral endoscope video dataset. Compared to other mainstream feature extraction algorithms, the method proposed in this paper is more accurate and comprehensive in identifying urethral vascular features, resulting in a 4.34% accuracy improvement, which confirms its effectiveness.