利用深度学习识别内镜图像中的巴雷特食管。
Identification of Barrett's esophagus in endoscopic images using deep learning.
机构信息
Department of Digestion, West China Hospital of Sichuan University, Chengdu, 610054, Sichuan, China.
Department of Digestion, The Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region, Ximianqiao Street No.20, Chengdu, 610054, Sichuan, China.
出版信息
BMC Gastroenterol. 2021 Dec 17;21(1):479. doi: 10.1186/s12876-021-02055-2.
BACKGROUND
Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images.
METHODS
443 endoscopic images from 187 patients of BE were included in this study. The gastroesophageal junction (GEJ) and squamous-columnar junction (SCJ) of BE were manually annotated in endoscopic images by experts. Fully convolutional neural networks (FCN) were developed to automatically identify the BE scopes in endoscopic images. The networks were trained and evaluated in two separate image sets. The performance of segmentation was evaluated by intersection over union (IOU).
RESULTS
The deep learning method was proved to be satisfying in the automated identification of BE in endoscopic images. The values of the IOU were 0.56 (GEJ) and 0.82 (SCJ), respectively.
CONCLUSIONS
Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the BE scope in endoscopic images. This automated recognition method helps clinicians to locate and recognize the scopes of BE in endoscopic examinations.
背景
开发一种深度学习方法，以识别内窥镜图像中的 Barrett 食管 (BE) 范围。
方法
本研究纳入了 187 名 BE 患者的 443 张内窥镜图像。专家对手册内窥镜图像中的胃食管交界处 (GEJ) 和鳞柱状交界处 (SCJ) 进行了手动注释。通过全卷积神经网络 (FCN) 自动识别内窥镜图像中的 BE 范围。网络在两个独立的图像集中进行训练和评估。通过交并比 (IOU) 评估分割性能。
结果
深度学习方法在自动识别内窥镜图像中的 BE 方面表现令人满意。IOU 的值分别为 0.56（GEJ）和 0.82（SCJ）。
结论
深度学习算法在 BE 范围的内窥镜图像分割方面具有较高的准确性，与手动人类评估具有一致性。这种自动识别方法有助于临床医生在内窥镜检查中定位和识别 BE 范围。
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