Teramoto Atsushi, Shibata Tomoyuki, Yamada Hyuga, Hirooka Yoshiki, Saito Kuniaki, Fujita Hiroshi
School of Medical Sciences, Fujita Health University, Toyoake 470-1192, Japan.
Department of Gastroenterology and Hepatology, Fujita Health University, Toyoake 470-1192, Japan.
Diagnostics (Basel). 2022 Aug 18;12(8):1996. doi: 10.3390/diagnostics12081996.
Endoscopy is widely applied in the examination of gastric cancer. However, extensive knowledge and experience are required, owing to the need to examine the lesion while manipulating the endoscope. Various diagnostic support techniques have been reported for this examination. In our previous study, segmentation of invasive areas of gastric cancer was performed directly from endoscopic images and the detection sensitivity per case was 0.98. This method has challenges of false positives and computational costs because segmentation was applied to all healthy images that were captured during the examination. In this study, we propose a cascaded deep learning model to perform categorization of endoscopic images and identification of the invasive region to solve the above challenges. Endoscopic images are first classified as normal, showing early gastric cancer and showing advanced gastric cancer using a convolutional neural network. Segmentation on the extent of gastric cancer invasion is performed for the images classified as showing cancer using two separate U-Net models. In an experiment, 1208 endoscopic images collected from healthy subjects, 533 images collected from patients with early stage gastric cancer, and 637 images from patients with advanced gastric cancer were used for evaluation. The sensitivity and specificity of the proposed approach in the detection of gastric cancer via image classification were 97.0% and 99.4%, respectively. Furthermore, both detection sensitivity and specificity reached 100% in a case-based evaluation. The extent of invasion was also identified at an acceptable level, suggesting that the proposed method may be considered useful for the classification of endoscopic images and identification of the extent of cancer invasion.
内镜检查在胃癌检查中应用广泛。然而，由于在操作内镜时需要检查病变，所以需要广泛的知识和经验。针对该检查已报道了各种诊断支持技术。在我们之前的研究中，直接从内镜图像中对胃癌浸润区域进行分割，每例的检测灵敏度为0.98。该方法存在假阳性和计算成本的挑战，因为分割应用于检查期间采集的所有正常图像。在本研究中，我们提出一种级联深度学习模型来进行内镜图像分类和浸润区域识别，以解决上述挑战。首先使用卷积神经网络将内镜图像分类为正常、显示早期胃癌和显示进展期胃癌。对于分类为显示癌症的图像，使用两个单独的U-Net模型对胃癌浸润范围进行分割。在一项实验中，从健康受试者收集的1208张内镜图像、从早期胃癌患者收集的533张图像以及从进展期胃癌患者收集的637张图像用于评估。所提出方法在通过图像分类检测胃癌中的灵敏度和特异性分别为97.0%和99.4%。此外，在基于病例的评估中，检测灵敏度和特异性均达到100%。浸润范围也被识别到可接受的水平，表明所提出的方法可能被认为对内镜图像分类和癌症浸润范围识别有用。
