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CMOS:OCT图像中的视网膜层的信任导向的多尺度半监督细分.

Junyu Shen, Chenggang Lu, Dan Zhang

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

此摘要是机器生成的。

我们开发了一种新的方法,用于在光学连贯断层扫描 (OCT) 图像中对视网膜层进行细分,即使在有限的数据和图像质量问题下,也提高了准确性和稳定性. 这促进了眼科疾病的诊断.

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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:

在光学连贯断层扫描 (OCT) 图像中,对视网膜层进行准确的细分对于诊断眼科疾病至关重要.
目前的方法在有限的注释数据和图像质量变化方面扎,特别是在患病的视网膜中.

研究的目的:

引入一种新型的半监督学习方法,以提高OCT视网膜层的细分.
在存在病变和成像不一致的情况下,提高细分精度和模型稳定性.

主要方法:

提出了以信任为导向的多尺度OCT细分 (CMOS) 方法.
整合了双向特征对齐以改进使用未标记数据的伪标签.
使用多尺度聚合 (MSA) 模块来处理特征变化和图像质量波动.

主要成果:

CMOS方法显著提高了OCT视网膜层细分的准确性.
与现有的半监督学习方法相比,表现优越.
在复杂的病理条件下和不同的图像质量下展示了改进的模型稳定性.

结论:

拟议的CMOS方法有效地解决了OCT视网膜层细分方面的关键挑战.
利用未标记的数据和多尺度特征可以实现最先进的性能.
这种方法在眼科医学的定量分析和诊断方面具有重大潜力.