基于血管注意特征 SLAM 的尿道内窥镜智能检查指导

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jie Lin, Xiangyu Zeng, Yulong Pan, Shangqing Ren, Yige Bao
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引用次数: 0

摘要

由于镜头成像范围小、操作过程中的抖动造成模糊以及不同位置观察到的尿道图像特征相似度高,医生在进行快速、全面的显微镜检查时经常面临挑战。本文结合图像处理、同步定位与映射(SLAM)和智能导航技术,构建了基于 ORB-SLAM 的辅助显微镜引导系统。它能自动处理实时显微镜视频,分析医生的检测路径,并为未检测到的区域提供方向指引,协助医生完成尿道壁检测。在该系统中,基于生成对抗网络的去模糊算法可在 SLAM 处理之前对尿道图像进行去模糊处理。我们创造性地提出了一种为尿道图像量身定制的基于血管注意力的特征提取算法。该算法结合了 F3Net 和 U-Net 网络,分别检测血管的主体和分支点,证明了该算法能够帮助 SLAM 系统更稳定地跟踪尿道。此外,我们还设计了方向引导规则,以帮助医生进行尿道内窥镜检查。我们利用真实的尿道内窥镜视频数据集对该系统进行了评估。与其他主流特征提取算法相比,本文提出的方法在识别尿道血管特征方面更加准确和全面,准确率提高了 4.34%,证实了其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Inspection Guidance of Urethral Endoscopy Based on SLAM with Blood Vessel Attentional Features

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.

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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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