Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, South Korea.
Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, South Korea; Department of Biomedical Engineering, Gachon University, Seongnam, 13120, South Korea.
Comput Biol Med. 2024 Oct;181:109038. doi: 10.1016/j.compbiomed.2024.109038. Epub 2024 Aug 22.
Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space constraints, incorporating depth cameras into endoscopic systems is often impractical. Our goal is to estimate depth images directly from endoscopic images using deep learning. This study presents a three-step methodology for training a depth-estimation network model. Initially, simulated endoscopy images and corresponding depth maps are generated using Unity based on a colon surface model obtained from segmented computed tomography colonography data. Subsequently, a cycle generative adversarial network model is employed to enhance the realism of the simulated endoscopy images. Finally, a deep learning model is trained using the synthesized endoscopy images and depth maps to estimate depths accurately. The performance of the proposed approach is evaluated and compared against prior studies utilizing unsupervised training methods. The results demonstrate the superior precision of the proposed technique in estimating depth images within endoscopy. The proposed depth estimation method holds promise for advancing the field by enabling enhanced navigation, improved lesion marking capabilities, and ultimately leading to better clinical outcomes.
在内窥镜检查中获取准确的距离或深度信息对于导航系统的有效利用至关重要。然而，由于空间限制，将深度相机集成到内窥镜系统中往往不切实际。我们的目标是使用深度学习直接从内窥镜图像中估计深度图像。本研究提出了一种三步法来训练深度估计网络模型。首先，使用基于 Unity 的模拟内窥镜图像和相应的深度图是根据从分割的 CT 结肠成像数据中获得的结肠表面模型生成的。随后，使用循环生成对抗网络模型来增强模拟内窥镜图像的逼真度。最后，使用合成的内窥镜图像和深度图训练深度学习模型以准确估计深度。评估了所提出方法的性能，并与使用无监督训练方法的先前研究进行了比较。结果表明，在所提出的技术中，在估计内窥镜中的深度图像方面具有更高的精度。所提出的深度估计方法有望通过增强导航、改善病变标记能力，并最终带来更好的临床结果来推动该领域的发展。
