AENEAS项目:通过使用机器视觉实时地标检测进行手术内解剖指导
Simone Olei1,2, Gary Sarwin3, Victor E Staartjes1
1Department of Neurosurgery, Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, University Hospital Zürich, Clinical Neuroscience Centre, University of Zürich, Zürich, Switzerland.
Mayo Clinic proceedings. Digital health
|February 11, 2026
概括
一个深度学习模型准确地识别了复杂的pterional trans-Sylvian手术期间的深度解剖标志. 这种机器视觉方法对改善外科导航和减少神经外科手术的变异性非常有希望.
科学领域:
- 神经外科 神经外科
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 微外科手术,如体跨西尔维亚方法,需要精确识别解剖结构.
- 术内导向可能具有挑战性,可能导致手术变化和风险增加.
研究的目的:
- 评估深度学习对象检测模型 (YOLOv7x) 的性能,用于识别复杂的pterional trans-Sylvian方法中的关键解剖地标.
- 评估机器视觉技术在神经外科手术中的解剖识别的可行性.
主要方法:
- 一个深度学习物体检测模型 (YOLOv7x) 在78个手术视频中的5307个标记上被开发和训练.
- 解剖学目标包括视神经,内动脉,内皮质和皮质旋转被注释.
- 模型性能使用5倍交叉验证以平均精度 (AP50) 作为主要指标进行评估.
主要成果:
- 该模型展示了深层结构的有希望的检测准确性,在视神经的AP50达到0.73,在内动脉的AP50达到0.67.
- 检测表面结构,如dura和皮质旋转,其精度较低 (AP50范围:0.25-0.45).
- 性能变化归因于解剖学复杂性和数据限制.
结论:
- 基于机器视觉的解剖检测在复杂的神经外科环境中是可行的.
- 对于深层解剖学地标的高精度验证了这一方法,尽管结构不那么明显的挑战.
- 这项技术有可能增强手术内指导和标准化外科手术实践.
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