Novel automated non invasive detection of ocular surface squamous neoplasia using multispectral autofluorescence imaging
Novel automated non invasive detection of ocular surface squamous neoplasia using multispectral autofluorescence imaging
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DOI：
10.1016/j.jtos.2019.03.003
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发表时间：
2019-07-01
期刊：
影响因子：
6.4
通讯作者：
Goldys, Ewa M.
中科院分区：
文献类型：
--
作者：
Habibalahi, Abbas;Bala, Chandra;Goldys, Ewa M.
目的：方法：收集18例经组织病理学诊断为眼表鳞状上皮瘤变（OSSN）的患者。他们以前收集的OSSN活检标本进行再处理，不染色，以获得自体荧光多光谱显微镜图像。这项技术涉及一个定制的光谱成像系统，具有38个光谱通道。部署了患者间和患者内框架，以使用机器学习方法自动检测和描绘OSSN。不同的机器学习方法进行了评估，K近邻和支持向量机被选为首选分类器内和患者间的框架，分别。该技术的性能进行了评估对病理assessment.Results：定量分析的光谱图像提供了一个强大的多光谱信号之间的相对差异肿瘤和正常组织内每个病人（p < 0.0005）和患者之间（p < 0.001）。我们基于机器学习的全自动诊断方法产生了相对良好的肿瘤-非肿瘤界面的地图。这样的地图可以快速生成准实时和用于术中评估。一般来说，OSSN可以检测到使用多光谱分析在这里调查的所有患者。通过多光谱分析检测到的癌症边缘是在密切和合理的协议，在H&E部分中观察到的利润率在内部和患者间classifications. comparison：本研究表明，使用多光谱自体荧光成像检测和发现人类OSSN的边界的可行性。基于机器学习方法的多光谱图像的全自动分析为OSSN提供了一个有前途的诊断工具，可以转化为未来的临床应用。
Purpose: Diagnosing Ocular surface squamous neoplasia (OSSN) using newly designed multispectral imaging technique.Methods: Eighteen patients with histopathological diagnosis of Ocular Surface Squamous Neoplasia (OSSN) were recruited. Their previously collected biopsy specimens of OSSN were reprocessed without staining to obtain auto fluorescence multispectral microscopy images. This technique involved a custom-built spectral imaging system with 38 spectral channels. Inter and intra-patient frameworks were deployed to automatically detect and delineate OSSN using machine learning methods. Different machine learning methods were evaluated, with K nearest neighbor and Support Vector Machine chosen as preferred classifiers for intra- and inter-patient frameworks, respectively. The performance of the technique was evaluated against a pathological assessment.Results: Quantitative analysis of the spectral images provided a strong multispectral signature of a relative difference between neoplastic and normal tissue both within each patient (at p < 0.0005) and between patients (at p < 0.001). Our fully automated diagnostic method based on machine learning produces maps of the relatively well circumscribed neoplastic-non neoplastic interface. Such maps can be rapidly generated in quasi-real time and used for intraoperative assessment. Generally, OSSN could be detected using multispectral analysis in all patients investigated here. The cancer margins detected by multispectral analysis were in close and reasonable agreement with the margins observed in the H&E sections in intra- and inter-patient classification, respectively.Conclusions: This study shows the feasibility of using multispectral auto-fluorescence imaging to detect and find the boundary of human OSSN. Fully automated analysis of multispectral images based on machine learning methods provides a promising diagnostic tool for OSSN which can be translated to future clinical applications.