Deep learning-enabled, non-invasive virtual histology of skin using reflectance confocal microscopy
Deep learning-enabled, non-invasive virtual histology of skin using reflectance confocal microscopy
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使用反射共焦显微镜进行深度学习、非侵入性皮肤虚拟组织学
DOI：
10.1117/12.2632602
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Rivenson, Yair
中科院分区：
文献类型：
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作者：
Li, Jingxi;Garfinkel, Jason;Zhang, Xiaoran;Wu, Di;Zhang, Yijie;de Haan, Kevin;Wang, Hongda;Liu, Tairan;Bai, Bijie;Rivenson, Yair
反射共聚焦显微镜（RCM）可以提供具有细胞水平分辨率的皮肤活体图像;然而，RCM图像是灰度的，缺乏核特征，并且与组织学的相关性低。我们提出了一种基于深度学习的虚拟染色方法，以基于体内无标记RCM图像执行皮肤的非侵入性虚拟组织学。这种虚拟组织学框架揭示了对各种皮肤状况的成功推断，例如基底细胞癌，也覆盖不同的皮肤层，包括表皮和真皮-表皮连接部。这种方法可以为更快，更准确地诊断恶性皮肤肿瘤铺平道路，同时减少不必要的活检。
Reflectance confocal microscopy (RCM) can provide in vivo images of the skin with cellular-level resolution; however, RCM images are grayscale, lack nuclear features and have a low correlation with histology. We present a deep learning-based virtual staining method to perform non-invasive virtual histology of the skin based on in vivo, label-free RCM images. This virtual histology framework revealed successful inference for various skin conditions, such as basal cell carcinoma, also covering distinct skin layers, including epidermis and dermal-epidermal junction. This method can pave the way for faster and more accurate diagnosis of malignant skin neoplasms while reducing unnecessary biopsies.