改进了YOLOv8-seg,用于在医学图像中识别喉结构
Haipo Cui1, Jinjing Wu1, Tianying Li2
1School of Health Science and Engineering, University of Shanghai for Science and Technology Shanghai 201210, China.
American journal of translational research
|June 19, 2025
概括
这项研究介绍了SlimMSDA-YOLO,这是一种高效的AI模型,用于在气管输管过程中实时识别喉结构. 该模型有助于临床医生识别关键解剖学,以改善患者的治疗结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 气管管道输入对于临床和紧急情况下的呼吸道管理至关重要.
- 精确识别喉结构 (上,喉,声带) 对于成功的输管术至关重要.
- 现有的方法可能缺乏复杂案例所需的实时精度.
研究的目的:
- 使用改进的YOLOv8-seg模型开发实时喉结构识别方法.
- 为了提高在输管过程中识别关键解剖标志的效率和准确性.
- 为临床医生提供轻量级和有效的工具.
主要方法:
- 利用了输管手术中的回顾性喉图像.
- 通过将轻量级的"Slimneck"结构集成到YOLOv8n-seg.中,开发了SlimMSDA-YOLO.
- 集成GSConv和标准卷曲以减少计算负载.
- 添加了一个多尺度扩展注意模块来改善特征捕获.
主要成果:
- SlimMSDA-YOLO实现了90.4%的精度,84.2%的回忆率和90.1%的mAP50.
- 该模型表现出高效率,每秒运行11.4 Giga浮点操作.
- 它具有3,139,819的低参数数,表明轻量级的设计.
结论:
- SlimMSDA-YOLO是一个轻量级和高效的实时喉结构识别模型.
- 在比较实验中,它的性能优于其他轻量级细分网络.
- 该模型适用于气管管道输入手术的临床应用.
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