Significance: Colorectal cancer incidence has decreased largely due to detection and removal of polyps. Computer-aided diagnosis development may improve on polyp detection and discrimination. Aim: To advance detection and discrimination using currently available commercial colonoscopy systems, we developed a deep neural network (DNN) separating the color channels from images acquired under narrow-band imaging (NBI) and white-light endoscopy (WLE). Approach: Images of normal colon mucosa and polyps from colonoscopies were studied. Each color image was extracted based on the color channel: red/green/blue. A multilayer DNN was trained using one-channel, two-channel, and full-color images. The trained DNN was then tested for performance in detection of polyps. Results: The DNN performed better using full-colored NBI over WLE images in the detection of polyps. Furthermore, the DNN performed better using the two-channel red + green images when compared to full-color WLE images. Conclusions: The separation of color channels from full-color NBI and WLE images taken from commercially available colonoscopes may improve the ability of the DNN to detect and discriminate polyps. Further studies are needed to better determine the color channels and combination of channels to include and exclude in DNN development for clinical use.

中文翻译：

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从传统结肠镜检查图像中分离颜色通道改进了息肉的深度神经网络检测

意义：由于息肉的发现和切除，结直肠癌的发病率已大大降低。计算机辅助诊断的发展可能会改善息肉的检测和辨别。目的：为了使用当前可用的商业结肠镜检查系统推进检测和鉴别，我们开发了一种深度神经网络 (DNN)，将颜色通道与在窄带成像 (NBI) 和白光内窥镜 (WLE) 下获取的图像分离。方法：研究来自结肠镜检查的正常结肠粘膜和息肉的图像。每个彩色图像都是根据颜色通道提取的：红/绿/蓝。使用单通道、双通道和全彩色图像训练多层 DNN。然后测试经过训练的 DNN 在检测息肉方面的性能。结果：DNN 在息肉检测中使用全彩色 NBI 优于 WLE 图像。此外，与全彩色 WLE 图像相比，使用双通道红+绿图像的 DNN 表现更好。结论：从商用结肠镜获取的全彩色 NBI 和 WLE 图像中分离颜色通道可能会提高 DNN 检测和区分息肉的能力。需要进一步的研究来更好地确定颜色通道和通道组合，以便在 DNN 开发中包括和排除临床使用。从市售结肠镜获取的全彩色 NBI 和 WLE 图像中分离颜色通道可能会提高 DNN 检测和区分息肉的能力。需要进一步的研究来更好地确定颜色通道和通道组合，以便在 DNN 开发中包括和排除临床使用。从市售结肠镜获取的全彩色 NBI 和 WLE 图像中分离颜色通道可能会提高 DNN 检测和区分息肉的能力。需要进一步的研究来更好地确定颜色通道和通道组合，以便在 DNN 开发中包括和排除临床使用。