使用自动图像分析算法在体内反射共聚焦显微镜图像上探索表皮结构的年龄相关变化
Imane Lboukili1,2, Georgios N Stamatas1, Xavier Descombes2
1R&D Essential Health, Johnson & Johnson Santé Beauté France, Issy-les-moulineaux, France.
概括
使用过器的自动化方法在显微镜图像中识别了表皮细胞,揭示了儿童和成人肌肤的动态成熟. 这种技术使得大规模分析皮肤生理学和老化. (37个单词) 的意思.
科学领域:
- 皮肤病学 皮肤病学
- 医疗成像医学成像
- 计算生物学 计算生物学
背景情况:
- 反射共聚焦显微镜 (RCM) 能够在体内进行表皮结构的非侵入性可视化.
- 对细胞参数的RCM图像进行手动分析是耗时且容易出现错误的.
- 为了高效的RCM图像分析,需要自动化细胞识别.
研究的目的:
- 开发和验证RCM图像中细胞识别的自动化算法.
- 用自动化方法分析儿童和成人表皮结构变化.
- 通过细胞和结构参数量化皮肤成熟和衰老过程.
主要方法:
- 兴趣区域和单个细胞识别使用Sato和Gabor过器.
- 后处理以改善细胞检测和异常标志的去除.
- 应用到儿童和成人的5345张RCM图像;使用深度学习计算细胞面积,周长,密度和表皮层厚度.
主要成果:
- 皮表皮质角质细胞的大小随着年龄的增长在儿童和成年人中增加.
- 皮肤成熟是整个童年和成年期的一个动态过程.
- 角膜层和上皮层的厚度随着年龄的增长而增加,在儿童中更快.
结论:
- 自动化方法有效地分析大型RCM数据集的皮肤生理学参数.
- 这些发现证实了儿童时期皮肤的动态成熟和成年时的皮肤衰老.
- 这种方法促进了对皮肤发育和老化的大规模研究.
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