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首页> 美国卫生研究院文献>Frontiers in Oncology >Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
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Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

机译：神经外科影像学治疗学的前景：通过深度学习增强共焦激光内窥镜诊断

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Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. CLE images can be distorted by motion artifacts, fluorescence signals out of detector dynamic range, or may be obscured by red blood cells, and thus interpreted as nondiagnostic (ND). However, just a single CLE image with a detectable pathognomonic histological tissue signature can suffice for intraoperative diagnosis. Dealing with the abundance of images from CLE is not unlike sifting through a myriad of genes, proteins, or other structural or metabolic markers to find something of commonality or uniqueness in cancer that might indicate a potential treatment scheme or target. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/ND, gliomaonglioma, tumor/injuryormal categories, and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow, and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.
机译：共聚焦激光内窥镜检查（CLE）是一种先进的光学荧光成像技术，由于其亚细胞尺寸分辨率，具有提高术中精度，扩大切除范围和针对恶性浸润性脑瘤进行手术的潜力。尽管具有潜在的诊断潜力，但对于未经培训的用户来说，难以理解灰度荧光图像。 CLE图像可能会因运动伪影而失真，荧光信号超出检测器动态范围，或者可能被红细胞遮盖，因此被解释为非诊断性（ND）。但是，只有具有可检测的病理组织学组织特征的单个CLE图像即可满足术中诊断的需要。处理来自CLE的大量图像与筛选无数的基因，蛋白质或其他结构或代谢标记物相似，以寻找可能表明潜在治疗方案或目标的癌症的共性或独特性。在本文中，我们详细介绍了CLE图像的生物信息学分析方法，该方法开始协助神经外科医生和病理学家将术中术中即时成像，病理学和手术观察快速连接到诊断学概念内的结论系统中。我们概述并讨论了用于自动检测诊断性CLE图像的深度学习模型，并讨论了各种训练方案和集成建模对深度学习预测模型的作用。本文回顾的两种主要方法包括可以将CLE图像自动分类为诊断/ ND，神经胶质瘤/非神经胶质瘤，肿瘤/损伤/正常类别的模型，以及可以使用弱监督方法对CLE图像进行组织学定位的模型。我们还简要回顾了用于其他器官的CLE图像分析的深度学习方法的进展。自动诊断框架选择的速度和精度的重大进步将增加CLE的诊断潜力，改善手术流程，并整合到脑肿瘤手术中。此类技术和生物信息学分析可提高脑肿瘤治疗的准确性，个性化和诊断学。

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