Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China.
Kiang Wu Hospital, Macau 999078, China.
Sensors (Basel). 2022 Feb 15;22(4):1492. doi: 10.3390/s22041492.
It is challenging for endoscopists to accurately detect esophageal lesions during gastrointestinal endoscopic screening due to visual similarities among different lesions in terms of shape, size, and texture among patients. Additionally, endoscopists are busy fighting esophageal lesions every day, hence the need to develop a computer-aided diagnostic tool to classify and segment the lesions at endoscopic images to reduce their burden. Therefore, we propose a multi-task classification and segmentation (MTCS) model, including the Esophageal Lesions Classification Network (ELCNet) and Esophageal Lesions Segmentation Network (ELSNet). The ELCNet was used to classify types of esophageal lesions, and the ELSNet was used to identify lesion regions. We created a dataset by collecting 805 esophageal images from 255 patients and 198 images from 64 patients to train and evaluate the MTCS model. Compared with other methods, the proposed not only achieved a high accuracy (93.43%) in classification but achieved a dice similarity coefficient (77.84%) in segmentation. In conclusion, the MTCS model can boost the performance of endoscopists in the detection of esophageal lesions as it can accurately multi-classify and segment the lesions and is a potential assistant for endoscopists to reduce the risk of oversight.
由于患者之间不同病变在形状、大小和纹理等方面存在视觉相似性，内镜医生很难在胃肠内镜筛查中准确检测到食管病变。此外，内镜医生每天都忙于与食管病变作斗争，因此需要开发一种计算机辅助诊断工具，以便对内镜图像中的病变进行分类和分割，以减轻他们的负担。因此，我们提出了一种多任务分类和分割（MTCS）模型，包括食管病变分类网络（ELCNet）和食管病变分割网络（ELSNet）。ELCNet 用于分类食管病变类型，ELSNet 用于识别病变区域。我们通过收集 255 名患者的 805 张食管图像和 64 名患者的 198 张图像来创建数据集，以训练和评估 MTCS 模型。与其他方法相比，该方法不仅在分类方面达到了很高的准确率（93.43%），而且在分割方面达到了较高的骰子相似系数（77.84%）。总之，MTCS 模型可以提高内镜医生检测食管病变的性能，因为它可以准确地对病变进行多分类和分割，是内镜医生减少漏诊风险的潜在辅助手段。
