Kim Hyeon-Seo, Cho Byungwoo, Park Jong-Oh, Kang Byungjeon
Graduate School of Data Science, Chonnam National University, Gwangju 61186, Republic of Korea.
Korea Institute of Medical Microrobotics, Gwangju 61011, Republic of Korea.
Diagnostics (Basel). 2024 Mar 11;14(6):591. doi: 10.3390/diagnostics14060591.
While the adoption of wireless capsule endoscopy (WCE) has been steadily increasing, its primary application remains limited to observing the small intestine, with relatively less application in the upper gastrointestinal tract. However, there is a growing anticipation that advancements in capsule endoscopy technology will lead to a significant increase in its application in upper gastrointestinal examinations. This study addresses the underexplored domain of landmark identification within the upper gastrointestinal tract using WCE, acknowledging the limited research and public datasets available in this emerging field. To contribute to the future development of WCE for gastroscopy, a novel approach is proposed. Utilizing color transfer techniques, a simulated WCE dataset tailored for the upper gastrointestinal tract is created. Using Euclidean distance measurements, the similarity between this color-transferred dataset and authentic WCE images is verified. Pioneering the exploration of anatomical landmark classification with WCE data, this study integrates similarity evaluation with image preprocessing and deep learning techniques, specifically employing the DenseNet169 model. As a result, utilizing the color-transferred dataset achieves an anatomical landmark classification accuracy exceeding 90% in the upper gastrointestinal tract. Furthermore, the application of sharpen and detail filters demonstrates an increase in classification accuracy from 91.32% to 94.06%.
虽然无线胶囊内镜（WCE）的应用一直在稳步增加，但其主要应用仍局限于观察小肠，在上消化道的应用相对较少。然而，越来越多的人预期胶囊内镜技术的进步将使其在上消化道检查中的应用显著增加。本研究探讨了使用WCE在上消化道内进行地标识别这一尚未充分探索的领域，认识到在这个新兴领域中可用的研究和公共数据集有限。为了推动用于胃镜检查的WCE的未来发展，提出了一种新方法。利用颜色转移技术，创建了一个针对上消化道定制的模拟WCE数据集。使用欧几里得距离测量，验证了这个颜色转移数据集与真实WCE图像之间的相似性。本研究率先利用WCE数据探索解剖地标分类，将相似性评估与图像预处理和深度学习技术相结合，具体采用DenseNet169模型。结果，利用颜色转移数据集在上消化道中实现了解剖地标分类准确率超过90%。此外，锐化和细节滤镜的应用使分类准确率从91.32%提高到了94.06%。
