A novel system design for monocular laser assisted capsule endoscopy for 3D small bowel modeling.

内容摘要

The long, tortuous, and tissue-homogeneous structure of the small bowel makes image-based three-dimensional (3D) modeling studies technically complex. Since wireless capsule endoscopy (WCE) systems generally produce only two-dimensional images, obtaining precise 3D structures with this limited data is highly challenging with existing traditional methods. In this study, we propose a novel capsule endoscopy prototype that enables 3D reconstruction of the small bowel from consecutive monocular images. Laser points that are circumferentially scanned and thus projected onto the intestinal surface are segmented using HSV color space transformation and area-based mask generation. The detected point coordinates are then sequentially placed according to the image order to generate a dense 3D point cloud. Unlike traditional methods, the proposed approach reconstructs 3D structure using only monocular images, without relying on stereo vision or multi-view algorithms. This point cloud can be used for visual analysis and modeling studies by reflecting the basic geometric properties of the intestinal surface. The results show that the proposed system achieves geometrically consistent 3D bowel model reconstruction with a total root mean square error (RMSE) of approximately ± 0.3 cm. First, a 3D point cloud was generated in the Python environment using sequential laser point coordinates. The point cloud generated in the Python environment achieved an RMSE of 2.27 mm. The same point cloud was then imported into CloudCompare software for independent validation. An RMSE of 3.13 mm was calculated in a standard 3D analysis tool. These results demonstrate that the proposed method achieves geometrically consistent and quantitistically reliable reconstruction under controlled experimental conditions. The proposed framework is evaluated as a proof-of-concept using controlled inter-frame depth displacement assumptions. The 3D modeling study was also conducted using Agisoft Metashape software. Additionally, the SIFT-SfM (Scale-Invariant Feature Transform-Structure-from-Motion) and ORB-SfM (Oriented FAST and Rotated BRIEF Structure-from-Motion) methods were applied in the Python environment. However, both approaches failed to produce a consistent model on the phantom data. These results demonstrate the suitability of the proposed laser-assisted monocular capsule endoscopy approach in challenging anatomical environments where visual features are limited. Additionally, we conducted a soft-tissue phantom experiment to evaluate the method under more anatomically realistic surface conditions, where the system successfully captured the general fold patterns of the tissue. The presented framework provides an initial experimental basis for future deep learning-based surface reconstruction and clinical validation studies. The processing pipeline achieves real-time-capable throughput under offline evaluation conditions. Furthermore, by providing a practical solution with low hardware requirements, it represents an important first step toward the development of more advanced and clinically applicable 3D modeling systems.