SAMCF:适应性全球风格对齐和多色空间融合,用于联合光杯和光盘细分
Longjun Huang1, Ningyi Zhang1, Yugen Yi1
1School of Software, Nanchang Key Laboratory for Blindness and Visual Impairment Prevention Technology and Equipment, Jiangxi Normal University, Nanchang, 330022, China.
Computers in biology and medicine
|June 15, 2024
概括
这项研究引入了一种新的框架 (SAMCF),用于在视网膜图像中对光杯和光盘进行细分. SAMCF通过解决数据集变化,亮度干扰和边缘感知挑战来提高准确性.
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:
- 在视网膜底部图像中,光杯 (OC) 和光盘 (OD) 的准确细分对于诊断眼睛疾病至关重要.
- 对于联合OC和OD细分的深度学习模型面临着由于数据集可变性,亮度干扰和边缘定义差的挑战.
研究的目的:
- 提出一种新的框架,即风格对齐和多色融合 (SAMCF),用于强大的联合OC和OD细分.
- 解决现有细分方法中的域移动,颜色表示限制和边缘感知问题.
主要方法:
- 开发了一个域泛化技术,用于统一的图像造型,以减轻数据集差异.
- 实现了多色空间特征提取和融合网络,以增强颜色表示和处理亮度变化.
- 整合了边缘感知损失功能,以提高细分边缘的清晰度和光滑性.
主要成果:
- 拟议的SAMCF框架在三个公共数据集 (DGS,RIM,REFUGE) 上,与最先进的方法相比,实现了更高的性能.
- SAMCF在各种视网膜底图像数据集中表现出了显著的概括能力.
结论:
- SAMCF有效地解决了联合OC和OD细分的关键挑战,提供了更高的准确性和稳定性.
- 该框架显示了通过增强的视网膜图像分析在眼病诊断中的临床应用的巨大潜力.
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