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基于深度学习的血管自动识别,用于腹腔镜右半球切除术.

Kyoko Ryu1,2,3, Daichi Kitaguchi1,2, Kei Nakajima1,2

  • 1Surgical Device Innovation, National Cancer Center Hospital East, Chiba, Japan.

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概括
此摘要是机器生成的。

一个新的深度学习模型准确地识别了腹腔镜右侧血液切除术 (RHC) 对于结肠癌的主要血管. 这种人工智能工具帮助外科医生可视化关键解剖,以获得更安全的手术和改进淋巴结剖析.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.laparoscopic 的右侧半球切除术.语义细分 语义细分是指语义细分.船舶识别系统 船舶识别系统

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科学领域:

  • 手术瘤学手术瘤学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 在右侧结肠癌中,精确的血管解剖学识别对于腹腔镜右半结肠切除术 (RHC) 至关重要.
  • 在识别用于淋巴结解剖和手术安全的主要血管方面存在挑战.

研究的目的:

  • 开发和评估一个深度学习 (DL) 模型,用于在腹腔镜RHC时自动识别和可视化主要血管.

主要方法:

  • 一个回顾性可行性研究,涉及使用DL模型对上层介质静脉 (SMV),大动脉 (ICA) 和大静脉 (ICV) 的语义细分.
  • 模型性能通过使用Dice系数,回忆和精度通过五倍交叉验证来评估.
  • 临床适用性由13名外科医生使用分级标签来评估.

主要成果:

  • 该DL模型实现了高精度的中小企业识别 (指标>0.75).
  • 对ICA和ICV的识别精度在0.53到0.57之间.
  • 外科医生为模型的临床应用潜力提供了可接受的评级.

结论:

  • 一个基于DL的血管细分模型已成功开发用于腹腔镜RHC.
  • 该模型展示了主要血管的可行识别和可视化.
  • 这种工具有可能提高RHC手术过程中的外科导航和安全性.