使用深度学习进行动脉瘤分析
Alireza Bagheri Rajeoni1, Breanna Pederson2, Susan M Lessner3
1Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA.
medRxiv : the preprint server for health sciences
|July 16, 2025
概括
本研究引入了一种使用人工智能精确测量动脉瘤体积的自动化方法. 该方法准确计算动脉瘤体积,改善患者的风险评估和治疗规划.
科学领域:
- 医学成像分析 医学成像分析
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 目前对动脉瘤的临床评估依赖于手动直径测量,这是耗时的,缺乏详细的形态洞察力.
- 动脉瘤体积测量提供了比单独的直径更好的了解疾病进展和严重程度.
- 现有的方法缺乏有效的自动化解决方案来量化动脉瘤体积.
研究的目的:
- 开发和验证一种自动化的方法,用于精确的动脉瘤边界划定和体积计算.
- 在未注释的临床数据集上评估新型人工智能驱动方法的性能.
- 在动脉瘤评估中减少依赖手动细分和观察者之间的变异性.
主要方法:
- 使用预先训练的UNet进行自动大动脉定位.
- 采用SAM2来通过复杂的血管结构追踪大动脉,包括动脉瘤.
- 应用长短期记忆 (LSTM) 网络或专家系统来识别动脉瘤开始和结束点.
- 在60名患者的数据集上计算了动脉瘤体积,没有像素智能的大动脉细分.
主要成果:
- 在预测动脉瘤开始 (R2=71%) 和结束点 (R2=76%) 方面取得了高准确性.
- 在动脉瘤体积预测方面表现出色,R2得分为92%.
- 在没有注释的数据集上验证了该方法的有效性,突出了其实际适用性.
结论:
- 拟议的自动化方法为动脉瘤体积测量提供了精确有效的解决方案.
- 这种人工智能驱动的技术有可能显著提高大规模动脉瘤分析.
- 这种方法有望通过提供可靠的体积数据和减少手工劳动来改善临床决策.
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