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# Dense Surface Reconstruction for Enhanced Navigation in MIS

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Recent introduction of dynamic view expansion has led to the development of computer vision methods for minimally invasive surgery to artificially expand the intra-operative field-of-view of the laparoscope. This provides improved awareness of the surrounding anatomical structures and minimises the effect of disorientation during surgical navigation. It permits the augmentation of live laparoscope images with information from previously captured views. Current approaches, however, can only represent the tissue geometry as planar surfaces or sparse 3D models, thus introducing noticeable visual artefacts in the final rendering results. This paper proposes a high-fidelity tissue geometry mapping by combining a sparse SLAM map with semi-dense surface reconstruction. The method is validated on phantom data with known ground truth, as well as in-vivo data captured during a robotic assisted MIS procedure. The derived results have shown that the method is able to effectively increase the coverage of the expanded surgical view without compromising mapping accuracy.
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... Dense SLAM methods have also been developed to generate dense tissue models in real-time. Totz et al [32] proposed an EKF-SLAM-based method for dense reconstruction. EKF-SLAM suffers from low accuracy and is difficult for representing loop closing. ...
Preprint
We propose an approach to reconstruct dense three-dimensional (3D) model of tissue surface from stereo optical videos in real-time, the basic idea of which is to first extract 3D information from video frames by using stereo matching, and then to mosaic the reconstructed 3D models. To handle the common low texture regions on tissue surfaces, we propose effective post-processing steps for the local stereo matching method to enlarge the radius of constraint, which include outliers removal, hole filling and smoothing. Since the tissue models obtained by stereo matching are limited to the field of view of the imaging modality, we propose a model mosaicking method by using a novel feature-based simultaneously localization and mapping (SLAM) method to align the models. Low texture regions and the varying illumination condition may lead to a large percentage of feature matching outliers. To solve this problem, we propose several algorithms to improve the robustness of SLAM, which mainly include (1) a histogram voting-based method to roughly select possible inliers from the feature matching results, (2) a novel 1-point RANSAC-based P$n$P algorithm called as DynamicR1PP$n$P to track the camera motion and (3) a GPU-based iterative closest points (ICP) and bundle adjustment (BA) method to refine the camera motion estimation results. Experimental results on ex- and in vivo data showed that the reconstructed 3D models have high resolution texture with an accuracy error of less than 2 mm. Most algorithms are highly parallelized for GPU computation, and the average runtime for processing one key frame is 76.3 ms on stereo images with 960x540 resolution.
... The concept of non-rigid SLAM was proposed in the DynamicFusion work [20], and is now an emerging topic in the computer vision field. Unlike the traditional rigid SLAM methods that estimate the 6-DoF rigid motion of the camera [21], non-rigid SLAM estimates the deformation and motion of the environment with respect to the camera, which usually has high degrees of freedom. Our 2D non-rigid SLAM method considers the 2D image mosaic as the environment map, which is similar to the 3D point cloud built by traditional 3D SLAM methods. ...
Preprint
Full-text available
The ability to extend the field of view of laparoscopy images can help the surgeons to obtain a better understanding of the anatomical context. However, due to tissue deformation, complex camera motion and significant three-dimensional (3D) anatomical surface, image pixels may have non-rigid deformation and traditional mosaicking methods cannot work robustly for laparoscopy images in real-time. To solve this problem, a novel two-dimensional (2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed in this paper, which is able to compensate for the deformation of pixels and perform image mosaicking in real-time. The key algorithm of this 2D non-rigid SLAM system is the expectation maximization and dual quaternion (EMDQ) algorithm, which can generate smooth and dense deformation field from sparse and noisy image feature matches in real-time. An uncertainty-based loop closing method has been proposed to reduce the accumulative errors. To achieve real-time performance, both CPU and GPU parallel computation technologies are used for dense mosaicking of all pixels. Experimental results on \textit{in vivo} and synthetic data demonstrate the feasibility and accuracy of our non-rigid mosaicking method.
... The first approach, used in traditional laparoscopy, is based on moving a monocular endoscope in order to reconstruct the 3D surface of the surgical area. Three methods are commonly used to obtain depth information: Structure from Motion (SfM) [3,26], SLAM [27,28], and Shape from Shading (SfS) [29]. However, a disadvantage for both SfM and SLAM is that the camera needs to move constantly in order to obtain 3D information. ...
Article
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... Mosaicking has recently gained attention to increase the FoV in fetoscopy [10,3,11,12,9]. Totz et al. [13] presented a dynamic view expansion and surface reconstruction approach for minimally invasive surgery by analyzing stereo laparoscopy videos. Reeff at al. [10] and Daga et al. [3] utilized a classical image feature-based matching method for creating mosaics from planar placenta images. ...
Chapter
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Chapter
We propose a novel stereo laparoscopy video-based non-rigid SLAM method called EMDQ-SLAM, which can incrementally reconstruct thee-dimensional (3D) models of soft tissue surfaces in real-time and preserve high-resolution color textures. EMDQ-SLAM uses the expectation maximization and dual quaternion (EMDQ) algorithm combined with SURF features to track the camera motion and estimate tissue deformation between video frames. To overcome the problem of accumulative errors over time, we have integrated a g2o-based graph optimization method that combines the EMDQ mismatch removal and as-rigid-as-possible (ARAP) smoothing methods. Finally, the multi-band blending (MBB) algorithm has been used to obtain high resolution color textures with real-time performance. Experimental results demonstrate that our method outperforms two state-of-the-art non-rigid SLAM methods: MISSLAM and DefSLAM. Quantitative evaluation shows an average error in the range of 0.8–2.2 mm for different cases.
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The ability to extend the field of view of laparoscopy images can help the surgeons to obtain a better understanding of the anatomical context. However, due to tissue deformation, complex camera motion and significant three-dimensional (3D) anatomical surface, image pixels may have non-rigid deformation and traditional mosaicking methods cannot work robustly for laparoscopy images in real-time. To solve this problem, a novel two-dimensional (2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed in this paper, which is able to compensate for the deformation of pixels and perform image mosaicking in real-time. The key algorithm of this 2D non-rigid SLAM system is the expectation maximization and dual quaternion (EMDQ) algorithm, which can generate smooth and dense deformation field from sparse and noisy image feature matches in real-time. An uncertainty-based loop closing method has been proposed to reduce the accumulative errors. To achieve real-time performance, both CPU and GPU parallel computation technologies are used for dense mosaicking of all pixels. Experimental results on in vivo and synthetic data demonstrate the feasibility and accuracy of our non-rigid mosaicking method.
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