Biopsy-free in vivo virtual histology of skin using deep learning.
Biopsy-free in vivo virtual histology of skin using deep learning.
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DOI：
10.1038/s41377-021-00674-8
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发表时间：
2021-11-18
期刊：
影响因子：
--
通讯作者：
Ozcan A
中科院分区：
文献类型：
--
作者：
Li J;Garfinkel J;Zhang X;Wu D;Zhang Y;de Haan K;Wang H;Liu T;Bai B;Rivenson Y;Rubinstein G;Scumpia PO;Ozcan A
浸润性活检后的组织学染色是皮肤肿瘤病理诊断的基准。这个过程既繁琐又耗时，往往会导致不必要的活组织检查和疤痕。新兴的非侵入性光学技术，如反射共聚焦显微镜（RCM），可以提供无标签、细胞级分辨率的活体皮肤图像，而无需进行活检。虽然RCM是一种有用的诊断工具，但它需要专门的训练，因为获得的图像是灰度的，缺乏核特征，并且难以与组织病理相关联。在这里，我们提出了一个基于深度学习的框架，该框架使用卷积神经网络将未染色皮肤的体内RCM图像快速转换为具有显微分辨率的虚拟染色苏木精和伊红样图像，从而实现表皮、真皮-表皮交界处和真皮表层的可视化。该网络在一种对抗学习方案下进行训练，该方案以切除的未染色/无标记组织的离体RCM图像作为输入，并使用醋酸核对比染色标记的同一组织的显微图像作为基础事实。我们证明，这种训练过的神经网络可以用于快速执行活体组织的虚拟组织学，正常皮肤结构、基底细胞癌和黑色素细胞痣的无标记RCM图像，显示出与来自相同切除组织的传统组织学相似的组织学特征。将基于深度学习的虚拟染色技术应用于非侵入性成像技术，可以更快地诊断恶性皮肤肿瘤，减少侵入性皮肤活检。
An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can provide label-free, cellular-level resolution, in vivo images of skin without performing a biopsy. Although RCM is a useful diagnostic tool, it requires specialized training because the acquired images are grayscale, lack nuclear features, and are difficult to correlate with tissue pathology. Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers. The network was trained under an adversarial learning scheme, which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. We show that this trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue. This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.
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影响因子：
3.7
作者：
Giacomelli MG;Husvogt L;Vardeh H;Faulkner-Jones BE;Hornegger J;Connolly JL;Fujimoto JG
通讯作者：
Fujimoto JG
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
13.8
作者：
Ko, Christine J.;Braverman, Irwin;Lowenstein, Eve J.
通讯作者：
Lowenstein, Eve J.
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
3.5
作者：
König, K;Riemann, I
通讯作者：
Riemann, I