RetOCTNet: 基于深度学习的OCT图像的细分 在视网膜质细胞损伤后
Gabriela Sanchez-Rodriguez1,2,3, Linjiang Lou2, Machelle T Pardue2,3,4
1Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Translational vision science & technology
|February 4, 2025
概括
深度学习工具RetOCTNet在受伤后的老鼠光学连贯性断层扫描中准确地细分了视网膜层. 这种自动化方法有助于在研究中监测视网膜神经纤维层厚度和视网膜厚度.
科学领域:
- 眼科医生 眼科 眼科
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
背景情况:
- 视网膜质细胞 (RGC) 损伤模型对于研究视神经病变至关重要.
- 视网膜层的精确细分,特别是视网膜神经纤维层 (RNFL) 和视网膜总厚度,对于量化RGC损失至关重要.
- 光学连贯断层扫描 (OCT) 扫描的手动细分是耗时的,容易引起观察者之间的变化.
研究的目的:
- 开发和验证RetOCTNet,这是一个深度学习工具,用于自动细分RNFL和来自老鼠OCT扫描的视网膜总厚度.
- 评估RetOCTNet在各种RGC损伤模型中的性能,包括眼高血压 (OHT) 和视神经压缩 (ONC).
- 评估RetOCTNet在OCT纵向体积扫描上的通用性.
主要方法:
- 在大鼠中,通过OHT或ONC诱导视网膜质细胞损伤.
- 从放射性OCT扫描中RNFL和视网膜总厚度的手动细分作为地面真相.
- RetOCTNet的训练和验证分别使用了80%和10%的手动细分.
- 该工具的通用性在基线和ONC后12周内从单独队列的体积扫描上进行了测试.
主要成果:
- RetOCTNet获得了高的F1分数:RNFL的0.88和对照眼睛的视网膜厚度的0.98.
- 在OHT和ONC眼中的细分也显示出高准确度 (F1分数:OHT的0.84/0.98,ONC的0.78/0.96).
- 对体积扫描的纵向分析显示ONC后显著的RNFL和视网膜稀释.
结论:
- RetOCTNet 精确地分段RNFL和总视网膜厚度,无论是辐射和体积的老鼠OCT扫描.
- 该工具在不同的伤害模型和扫描类型中展示了强度.
- RetOCTNet提供了一种可靠和有效的方法,用于在动物模型中纵向监测RGC损伤.
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