通过基于深度学习的OCT图像细分方法对in vivo细胞核和人类皮肤层进行定量评估
Chih-Hao Liu1, Li-Wei Fu2, Shu-Wen Chang1
1Graduate Institute of Photonics and Optoelectronics, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan.
Biomedical optics express
|May 5, 2025
概括
深度学习模型从光学连贯断层扫描 (OCT) 图像中准确地细分人类皮肤层和角质细胞核. 这使得皮肤结构的精确,非侵入性测量能够用于临床诊断.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 医学诊断 医学诊断 医学诊断
- 计算生物学 计算生物学
背景情况:
- 细胞分辨率光学连贯性断层扫描 (OCT) 提供了高分辨率的非侵入性诊断潜力.
- 皮肤层和细胞结构的准确细分对于定量分析至关重要.
研究的目的:
- 开发和验证一个深度学习模型,用于在OCT图像中对人类皮肤层和角质细胞核进行细分.
- 为了使皮肤结构的定量测量用于临床应用.
主要方法:
- 使用基于U-Net的深度学习框架为5类细分模型.
- 实施深度监督学习以单独优化全球 (皮肤层) 和本地 (核) 功能细分.
- 进行5倍交叉验证以评估模型性能.
主要成果:
- 在皮肤层和角质细胞核细分方面实现了>85%的圆盘系数准确度.
- 量化测量了角层厚度 (22.71 ± 17.20 微米),表皮厚度 (66.44 ± 11.61 微米) 和角质细胞核面积 (17.21 ± 9.33 微米2).
- 结果与健康人体皮肤的确定的临床研究结果一致.
结论:
- 开发的深度学习模型使用OCT提供了人类皮肤结构的准确和定量分析.
- 这种方法通过解决系统分辨率和注释变异性来提高细分精度.
- 这些发现支持OCT成像作为临床皮肤评估的标准化工具.
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