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OPTIMEyes平台显著改善了从 fundus自光图像的地理缩 (GA) 分段,减少了注释时间并提高了注释者之间的分段一致性,而不会影响准确性.

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科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 地理缩 (GA) 细分从 fundus 自体光对于监测疾病进展至关重要.
  • 手动细分是耗时的,并且可能会受到注释者之间的变化.

研究的目的:

  • 评估OPTIMEyes互动机器学习 (IML) 平台用于眼科图像注释.
  • 评估人工智能辅助细分的效率,统一性和非劣等性,与无辅助的地理缩 (GA) 细分相比.

主要方法:

  • 10位注释者对110张GA. fundus自光成像进行了细分.
  • 实地真相细分由专家视网膜专家提供.
  • 用注释时间和DICE分数来比较无辅助和人工智能辅助的细分.

主要成果:

  • 人工智能辅助将平均注释时间减少了96秒.
  • OPTIMEyes 改善了标注者之间的细分相似性 (平均 DICE 差异为 0.02).
  • 在具有挑战性的图像中观察到细分质量的显著改善 (平均DICE改进:0.38).

结论:

  • OPTIMEyes提高了GA细分效率和基底自光成像中的统一性.
  • 该平台缩短了注释时间,并改善了注释者之间的协议,而不会牺牲细分质量.
  • OPTIMEyes促进了研究和临床应用的高质量标记数据集的创建.