Son Geonhui, Eo Taejoon, An Jiwoong, Oh Dong Jun, Shin Yejee, Rha Hyenogseop, Kim You Jin, Lim Yun Jeong, Hwang Dosik
School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea.
Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea.
Diagnostics (Basel). 2022 Jul 31;12(8):1858. doi: 10.3390/diagnostics12081858.
By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to apply automated small bowel lesion detection algorithms based on deep learning. The purpose of the study was to develop an automated small bowel detection method from long untrimmed videos captured from WCE. Through this, the stomach and colon can also be distinguished. The proposed method is based on a convolutional neural network (CNN) with a temporal filtering on the predicted probabilities from the CNN. For CNN, we use a ResNet50 model to classify three organs including stomach, small bowel, and colon. The hybrid temporal filter consisting of a Savitzky-Golay filter and a median filter is applied to the temporal probabilities for the "small bowel" class. After filtering, the small bowel and the other two organs are differentiated with thresholding. The study was conducted on dataset of 200 patients (100 normal and 100 abnormal WCE cases), which was divided into a training set of 140 cases, a validation set of 20 cases, and a test set of 40 cases. For the test set of 40 patients (20 normal and 20 abnormal WCE cases), the proposed method showed accuracy of 99.8% in binary classification for the small bowel. Transition time errors for gastrointestinal tracts were only 38.8 ± 25.8 seconds for the transition between stomach and small bowel and 32.0 ± 19.1 seconds for the transition between small bowel and colon, compared to the ground truth organ transition points marked by two experienced gastroenterologists.
通过自动对胃、小肠和结肠进行分类，可以减少无线胶囊内镜检查（WCE）的阅片时间。此外，为了应用基于深度学习的自动小肠病变检测算法，定位小肠是必不可少的第一步预处理步骤。本研究的目的是从WCE拍摄的未修剪长视频中开发一种自动小肠检测方法。通过这种方法，还可以区分胃和结肠。所提出的方法基于卷积神经网络（CNN），并对CNN预测的概率进行时间滤波。对于CNN，我们使用ResNet50模型对包括胃、小肠和结肠在内的三个器官进行分类。将由Savitzky-Golay滤波器和中值滤波器组成的混合时间滤波器应用于“小肠”类别的时间概率。滤波后，通过阈值化区分小肠和其他两个器官。该研究在200例患者的数据集上进行（100例正常WCE病例和100例异常WCE病例），该数据集分为140例的训练集、20例的验证集和40例的测试集。对于40例患者的测试集（20例正常WCE病例和20例异常WCE病例），所提出的方法在小肠的二分类中显示出99.8%的准确率。与两位经验丰富的胃肠病学家标记的真实器官过渡点相比，胃肠道的过渡时间误差在胃和小肠之间仅为38.8±25.8秒，在小肠和结肠之间为32.0±19.1秒。
