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3D Reconstruction of Colon Structures and Textures from Colonoscopic Videos

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Colonoscopy is considered the most effective method for detecting and removing precan-cerous polyps in the human colon. This procedure uses an endoscope to examine the internal surface of the entire colon. However, during a standard colonoscopy, it can be challenging for the endoscopist to ensure that the entire colon internal surface is inspected from the colon screening video, which can result in missed polyps and adenomas in unin-spected regions. If a 3D map of the colon internal surface with detailed textures can be reconstructed during the colonoscopy procedure, the following two main potential benefits can be achieved: i) uninspected regions can be shown on this map and the endoscopist can navigate the endoscope to these missing regions to ensure more colon surfaces are inspected; ii) the detailed textures on the reconstructed map can help the endoscopist to inspect abnormalities offline.

In this dissertation, we present three works for reconstructing 3D colon maps from colono-scopic videos. Meanwhile, we introduce a colonoscopy simulator developed in Unity that can simulate the procedures of colonoscopy, different levels of colonic surface deformation, and generate synthetic colonoscopy datasets in different scenarios for the development and validation of colon reconstruction algorithms. Furthermore, to foster research in this field, the colonoscopy simulator and source code are made publicly available.

The first work presents a framework for 3D reconstruction of the colonic surface using stereo colonoscopic images. The input comprises a sequence of stereo colonoscopic images and a corresponding colon mesh model, which is segmented from pre-operative CT scans. The final output is the reconstructed and texturized 3D colon maps. The primary contribution of this work is fourfold: (1) Developing a visual odometry for endoscopic camera pose initialization; (2) Using the pre-operative CT-segmented colon model as a global colon map reference to increase the stability and accuracy of the endoscopic camera pose estimation; (3) Developing a joint photometric and geometric constrained scan-to-model registration algorithm for matching 3D scans (point cloud with RGB information and reconstructed from stereo images) to the pre-operative CT-segmented colon model, which can address the inconsistency of the texture matching problem; (4) Developing a barycentric-based texture rendering module for mapping textures from colonoscopic images to the reconstructed colonic surface. Simulation experimental results demonstrate the feasibility and good performance of the proposed 3D colonic surface reconstruction method in terms of accuracy and robustness.

In a clinical setting, the majority of colonoscopes used for colonoscopy procedures are equipped with a monocular camera. Meanwhile, the 3D reconstruction of colonic surface faces the problem of colon deformation. To improve the practicability of the first proposed framework, in the second work, we present a framework that can recover the 3D shape of deformable colon structures with textures from monocular colonoscopic images and a corresponding pre-operative CT-segmented colon mesh model. The novelty of the second work is threefold: (1) Using deep learning techniques to estimate dense depth for monocular colonoscopic images; (2) Developing a non-rigid registration method to address the problem of colon deformation; and (3) Developing the entire framework for the 3D reconstruction of deformable colonic surfaces with high accuracy. Validation by simulation and in-vivo experiments is conducted, and the results demonstrate the practicality of the non-rigid 3D colon reconstruction framework.

The third work is significantly differs from the previous two works, which require pair-wise photometric correspondences and dense geometric correspondences, posing a great challenge for low-textured colonoscopic images. In the third work, we formulate the textured colon reconstruction problem as a bundle adjustment (BA) problem where all the camera poses and the intensities of mesh model vertices are jointly optimized by maximizing the photometric consistency between the pre-operative CT-segmented colon mesh model and multiple views of colonoscopic images. Then, the optimized camera poses are used to render the colon map with textures from colonoscopic images. The novelty of this work is threefold: (1) Formulating simultaneous camera pose estimation as a direct BA problem, where the pre-operative model intensities and all camera poses are jointly optimized, which differs from traditional BA; (2) Directly using intensity information avoids the feature extraction and matching between 2D images in traditional BA, making the proposed method applicable to images lacking salient features, such as colonoscopic images; (3) We prove that when solving the proposed BA problem using the Gauss-Newton (GN) algorithm, the pose estimation result in each iteration of GN is independent of the model intensities in the previous iteration step, thus we propose the camera-only BA algorithm which is equivalent to the proposed direct BA algorithm but with less computational cost. The practicality and accuracy of the proposed direct camera-only BA method are validated using simulation, phantom, and in-vivo datasets.verall, the three frameworks proposed in this thesis represent a notable advancement in the field of 3D colonic surface reconstruction, using colonoscopic images and a pre-operative CT-segmented colon mesh model. These frameworks undergo validation through rigorous testing with simulation, phantom, and in-vivo datasets, demonstrating their feasibility, accuracy, and practicality. The clinical applications of these frameworks have the potential to enhance the accuracy and efficiency of colonic surface 3D reconstruction, thereby benefiting diagnosis, treatment planning, and surgical navigation in colonoscopy procedures.


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DepartmentDepartment of Computer Science and Engineering
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GB/T 7714
Zhang S. 3D Reconstruction of Colon Structures and Textures from Colonoscopic Videos[D]. 澳大利亚. 悉尼科技大学,2022.
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