MCPD-YOLOv3:一种新型的腹腔镜手术器械轻量化检测模型

IF 2.1 3区 医学 Q2 SURGERY
Yuqin Li, Chuqi Li, Ke Zhang, Yu Miao, Weili Shi, Zhengang Jiang
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引用次数: 0

摘要

背景在计算机辅助微创手术中,腹腔镜手术器械检测是必要的。现有的方法大多存在性能不理想、检测速度慢的问题。方法提出一种名为MCPD-YOLOv3的框架来平衡腹腔镜手术器械检测的效率和效果。它采用并行方式有效融合特征映射,并采用多种轻量级策略设计轻量级模型。此外,采用DIoU来提高召回性能。结果该方法在49.81 FPS下对ATLAS Dione和m2cai16-tool-locations数据集的mAP分别达到99.47%和97.65%,模型尺寸紧凑(12.4M), FLOPs数低(7.44G)。结论MCPD-YOLOv3具有检测性能高、反应速度快的特点。该模型在参数大小和FLOPs方面的效率证明了它适用于需要快速处理和精确检测的应用,使其成为在具有挑战性的环境中实时检测手术器械的宝贵工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MCPD-YOLOv3: A Novel Lightweight Detection Model for Surgical Instruments in Laparoscopic Images

Background

Laparoscopic surgical instruments detection is necessary in computer-aided minimally invasive surgery. Most current methods suffer from unsatisfied performance and low detection speed.

Methods

In this paper, a framework called MCPD-YOLOv3 is proposed to balance the efficiency and effectiveness of laparoscopic surgical instruments detection. It effectively fuses feature maps using a parallel manner, and adopts various lightweight strategies to design a lightweight model. Besides, DIoU is employed to improve the recall performance.

Results

The proposed method achieved the mAP of 99.47% and 97.65% at 49.81 FPS for the ATLAS Dione and m2cai16-tool-locations datasets, respectively, with a compact model size of 12.4M and a low FLOPs count of 7.44G.

Conclusion

These results highlight that MCPD-YOLOv3 excels in high detection performance and rapid response. The model's efficiency in parameter size and FLOPs demonstrates its suitability for applications requiring rapid processing and precise detection, making it a valuable tool for real-time surgical instrument detection in challenging environments.

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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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