Huttunen Mikko J, Hristu Radu, Dumitru Adrian, Floroiu Iustin, Costache Mariana, Stanciu Stefan G
Photonics Laboratory, Physics Unit, Tampere University, Tampere, Finland.
These authors contributed equally to this work.
Biomed Opt Express. 2019 Dec 10;11(1):186-199. doi: 10.1364/BOE.11.000186. eCollection 2020 Jan 1.
Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard for the diagnostics of many pathologies, including cancers. However, such approaches are laborious, time consuming and contain a risk for bias or human error. There is thus a clear need for faster, less intrusive and more accurate diagnostic solutions, requiring also minimal human intervention. Multiphoton microscopy (MPM) can alleviate some of the drawbacks specific to traditional histopathology by exploiting various endogenous optical signals to provide virtual biopsies that reflect the architecture and composition of tissues, both or . Here we show that MPM imaging of the dermoepidermal junction (DEJ) in unstained fixed tissues provides useful cues for a histopathologist to identify the onset of non-melanoma skin cancers. Furthermore, we show that MPM images collected on the DEJ, besides being easy to interpret by a trained specialist, can be automatically classified into healthy and dysplastic classes with high precision using a Deep Learning method and existing pre-trained convolutional neural networks. Our results suggest that deep learning enhanced MPM for skin cancer screening could facilitate timely diagnosis and intervention, enabling thus more optimal therapeutic approaches.
由训练有素的专家进行的组织病理学图像分析目前被视为包括癌症在内的许多疾病诊断的金标准。然而，这种方法费力、耗时，且存在偏差或人为错误的风险。因此，显然需要更快、侵入性更小且更准确的诊断解决方案，同时也需要最少的人工干预。多光子显微镜（MPM）可以通过利用各种内源性光学信号来提供反映组织结构和组成的虚拟活检，从而缓解传统组织病理学特有的一些缺点。在这里，我们表明，在未染色的固定组织中对真皮表皮交界处（DEJ）进行MPM成像为组织病理学家识别非黑色素瘤皮肤癌的发病提供了有用的线索。此外，我们表明，在DEJ上收集的MPM图像，除了易于训练有素的专家解读外，还可以使用深度学习方法和现有的预训练卷积神经网络高精度地自动分类为健康和发育异常类别。我们的结果表明，深度学习增强的MPM用于皮肤癌筛查可以促进及时诊断和干预，从而实现更优化的治疗方法。
