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在 fundus 中对光盘和光杯的细分模型的研究
Naigong Chen1,2,3, Xiujuan Lv4,5,6
1National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325000, China. cng@mail.eye.ac.cn.
BMC ophthalmology
|June 29, 2024
概括
一个改进的YOLOv8算法增强光盘和光杯在 fundus 图像中的细分,以早期检测青光眼. 与现有模型相比,这种方法显著提高了细分的准确性和速度.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 玻璃眼是全球不可逆转的视力丧失的主要原因.
- 早期发现青光眼对于预防视力障碍至关重要.
- 视网膜底图像分析,特别是光盘 (OD) 和光杯 (OC) 分段,有助于青光眼的诊断.
研究的目的:
- 为了提高视网膜底图像中的OD和OC的细分性能.
- 为了提高青光眼诊断算法的普遍性,性能和准确性.
主要方法:
- 修改了用于OD和OC细分的YOLOv8算法.
- 整合了一个兴趣区域 (ROI) 模块,并将损失函数更新为Focal-EIoU.
- 根据YOLOv8输入调整了REFUGE数据集,并评估了改进的模型.
主要成果:
- 改进的YOLOv8算法在REFUGE数据集上展示了出色的细分性能.
- 在OD和OC细分任务中获得了0.999的高F1得分.
- 在训练速度,细分性能和准确性方面表现优于U-Net模型.
结论:
- 增强的YOLOv8算法提供了卓越的OD和OC细分,用于青光眼的诊断.
- 这种改进的模型为分析 fundus 图像提供了更有效,更准确的解决方案.
- 这项研究强调了先进的深度学习技术在早期发现青光眼的潜力.
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