使用深度学习对口腔内窥镜检查图像中的癌组织进行自动分类。,Scientific Reports

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使用深度学习对口腔内窥镜检查图像中的癌组织进行自动分类。
Scientific Reports ( IF 3.8 ) Pub Date : 2017-Sep-20 , DOI: 10.1038/s41598-017-12320-8
Marc Aubreville , Christian Knipfer , Nicolai Oetter , Christian Jaremenko , Erik Rodner , Joachim Denzler , Christopher Bohr , Helmut Neumann , Florian Stelzle , Andreas Maier

口腔鳞状细胞癌（OSCC）是口腔上皮癌的一种常见类型。尽管它们对死亡率有很大影响，但是用于OSCC早期诊断的足够筛查方法通常缺乏准确性，因此OSCC大多在晚期被诊断出。OSCC的早期发现和准确的轮廓估计将导致更好的疗效，并减少手术治疗后的复发率。共聚焦激光内窥镜检查（CLE）记录亚表面的微观解剖图像，用于体内细胞结构分析。最近的CLE研究显示了OSCC实时可靠的超结构实时成像的广阔前景。我们介绍和评估一种新颖的自动方法，用于在CLE图像上使用深度学习技术进行OSCC诊断。将该方法与代表当前技术水平的基于纹理特征的机器学习方法进行了比较。对于这项工作，从口腔中的4个特定位置（包括OSCC病变）获得了诊断为OSCC的患者的CLE图像序列（7894张图像）。发现本方法在CLE图像识别方面优于现有技术，其曲线下面积（AUC）为0.96，平均准确度为88.3％（灵敏度为86.6％，特异性为90％）。

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Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning.

Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).

更新日期：2017-09-20

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