MFF-Net:用于颅内手术器械的多尺度特征融合语义分割网络。

IF 2.3 3区 医学 Q2 SURGERY
Zhenzhong Liu, Laiwang Zheng, Shubin Yang, Zichen Zhong, Guobin Zhang
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引用次数: 0

摘要

背景:在机器人辅助手术中,手术器械图像的自动分割对手术安全至关重要。所提出的方法通过有效的手术器械分割网络解决了开颅手术环境中的挑战,如遮挡和照明。方法:该网络使用YOLOv8作为目标检测框架,并集成语义分割头来实现检测和分割能力。设计了多通道特征图的级联,通过融合深度和浅层特征来增强模型的泛化能力。创新的GBC2f模块确保了网络的轻量级和捕获全球信息的能力。结果:颅内神经胶质瘤手术器械数据集的实验验证显示出优异的性能:94.9%的MPA评分、89.9%的MIoU值和126.6FPS。结论:根据实验结果,本研究提出的分割模型比其他最先进的模型具有显著优势。这为智能手术机器人的进一步发展提供了宝贵的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MFF-Net: Multiscale feature fusion semantic segmentation network for intracranial surgical instruments

Background

In robot-assisted surgery, automatic segmentation of surgical instrument images is crucial for surgical safety. The proposed method addresses challenges in the craniotomy environment, such as occlusion and illumination, through an efficient surgical instrument segmentation network.

Methods

The network uses YOLOv8 as the target detection framework and integrates a semantic segmentation head to achieve detection and segmentation capabilities. A concatenation of multi-channel feature maps is designed to enhance model generalisation by fusing deep and shallow features. The innovative GBC2f module ensures the lightweight of the network and the ability to capture global information.

Results

Experimental validation of the intracranial glioma surgical instrument dataset shows excellent performance: 94.9% MPA score, 89.9% MIoU value, and 126.6 FPS.

Conclusions

According to the experimental results, the segmentation model proposed in this study has significant advantages over other state-of-the-art models. This provides a valuable reference for the further development of intelligent surgical robots.

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来源期刊
CiteScore
4.50
自引率
12.00%
发文量
131
审稿时长
6-12 weeks
期刊介绍: The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.
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