通过细分识别眼镜杯和光盘的边缘在眼患者的光盘
Srikanth Tadisetty1, Ranjith Chodavarapu1, Ruoming Jin1
1Department of Computer Science, Kent State University, Kent, OH 44242, USA.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究使用人工智能自动检测光盘和光杯边缘在眼底图像中,用于青光眼的诊断. 人工智能模型显示,杯对盘比率分析的细分效率有前途.
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:
- fundus 疾病的早期诊断对于有效治疗至关重要.
- 青光眼的诊断通常依赖于从 fundus 图像中分析杯与盘的比率 (CDR).
- 使用人工智能对 fundus 图像进行自动化分析是一个不断增长的研究领域.
研究的目的:
- 为了检测光眼杯和光盘的边缘在眼患者的 fundus 图像.
- 应用修改后的U-Net模型来准确细分眼睛结构.
- 为了进一步分析玻璃眼对光盘比率 (CDR) 进行青光眼评估.
主要方法:
- 使用修改后的U-Net模型架构进行语义细分.
- 应用边缘检测和扩展技术用于后处理细分结果.
- 在多个基底图像数据集 (ORIGA,RIM-ONE v3,REFUGE,Drishti-GS) 上使用标准细分指标评估模型性能.
主要成果:
- 为光杯和光盘实现了有前途的细分效率.
- 证明了该模型在准确识别CDR分析的关键眼球结构方面的能力.
- 在多种不同的基底图像数据集中验证了方法.
结论:
- 拟议的人工智能驱动的方法提供了一种有效的方法,用于在 fundus 图像中自动细分光盘和光杯.
- 这种技术显示出通过CDR分析提高青光眼诊断的准确性和效率的巨大潜力.
- 进一步的研究可以建立在这个模型的基础上,以提高 fundus 疾病的早期检测.
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