Color-based hybrid reconstruction for endoscopy

Tokgozoglu, Haluk Noyan; Meisner, Eric; Kazhdan, Michael; Hager, Gregory D.

doi:10.1109/cvprw.2012.6239241

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…However, the magnetic levitation capsule cannot reach the ideal state in the process of movement, and the calibration accuracy needs to be further improved. In the same year, Tokgozoglu et al [8] proposed an SFS method based on color projection, which can minimize the intensity changes caused by different surface characteristics. In 2015, Goncalves et al [9] proposed a perspective shape from shading (PSFS) algorithm based on near light source perspective mapping to solve radial distortion and reduced image edge resolution.…”

Section: Introductionmentioning

confidence: 99%

3D Texture Reconstruction of Abdominal Cavity Based on Monocular Vision SLAM for Minimally Invasive Surgery

et al. 2022

Symmetry

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The depth information of abdominal tissue surface and the position of laparoscope are very important for accurate surgical navigation in computer-aided surgery. It is difficult to determine the lesion location by empirically matching the laparoscopic visual field with the preoperative image, which is easy to cause intraoperative errors. Aiming at the complex abdominal environment, this paper constructs an improved monocular simultaneous localization and mapping (SLAM) system model, which can more accurately and truly reflect the abdominal cavity structure and spatial relationship. Firstly, in order to enhance the contrast between blood vessels and background, the contrast limited adaptive histogram equalization (CLAHE) algorithm is introduced to preprocess abdominal images. Secondly, combined with AKAZE algorithm, the Oriented FAST and Rotated BRIEF(ORB) algorithm is improved to extract the features of abdominal image, which improves the accuracy of extracted symmetry feature points pair and uses the RANSAC algorithm to quickly eliminate the majority of mis-matched pairs. The medical bag-of-words model is used to replace the traditional bag-of-words model to facilitate the comparison of similarity between abdominal images, which has stronger similarity calculation ability and reduces the matching time between the current abdominal image frame and the historical abdominal image frame. Finally, Poisson surface reconstruction is used to transform the point cloud into a triangular mesh surface, and the abdominal cavity texture image is superimposed on the 3D surface described by the mesh to generate the abdominal cavity inner wall texture. The surface of the abdominal cavity 3D model is smooth and has a strong sense of reality. The experimental results show that the improved SLAM system increases the registration accuracy of feature points and the densification, and the visual effect of dense point cloud reconstruction is more realistic for Hamlyn dataset. The 3D reconstruction technology creates a realistic model to identify the blood vessels, nerves and other tissues in the patient’s focal area, enabling three-dimensional visualization of the focal area, facilitating the surgeon’s observation and diagnosis, and digital simulation of the surgical operation to optimize the surgical plan.

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Section: Introductionmentioning

confidence: 99%

3D Texture Reconstruction of Abdominal Cavity Based on Monocular Vision SLAM for Minimally Invasive Surgery

et al. 2022

Symmetry

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“…4. Shape-from-Shading methods are often combined with fusion techniques, such as [9,10,11], and often require careful photometric calibration to ensure accuracy. Reconstruction with tissue deformation are handled in [12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment

Liu

Stiber

Huang

et al. 2020

Preprint

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Reconstructing accurate 3D surface models of sinus anatomy directly from an endoscopic video is a promising avenue for cross-sectional and longitudinal analysis to better understand the relationship between sinus anatomy and surgical outcomes. We present a patient-specific, learning-based method for 3D reconstruction of sinus surface anatomy directly and only from endoscopic videos. We demonstrate the effectiveness and accuracy of our method on in and ex vivo data where we compare to sparse reconstructions from Structure from Motion, dense reconstruction from COLMAP, and ground truth anatomy from CT. Our textured reconstructions are watertight and enable measurement of clinically relevant parameters in good agreement with CT. The source code will be made publicly available upon publication.

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“…Although navigation is widely employed for FESS, its capabilities are far from optimal. The significance of endoscopic visualization [1,2] is inducing a paradigm shift in surgical navigation by using endoscope to improve anatomy registration. It provides an inexpensive, non-invasive, radiation-free method to enhance registration accuracy at any point of the procedure.…”

Section: Introductionmentioning

confidence: 99%

“…EM tracking can correct drifting, while frame-by-frame tracking-by-matching gives a refined location. Lastly, reconstruction can follow a point cloud generation by either simple triangulation [2,4] or bundle adjustment [5,6], or surface rendering methods using shading [1] and specularities [7]. …”

Section: Introductionmentioning

confidence: 99%

Is Multi-model Feature Matching Better for Endoscopic Motion Estimation?

Xiang

Mirota

Reiter

et al. 2014

Computer-Assisted and Robotic Endoscopy

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Camera motion estimation is a standard yet critical step to endoscopic visualization. It is affected by the variation of locations and correspondences of features detected in 2D images. Feature detectors and descriptors vary, though one of the most widely used remains SIFT. Practitioners usually also adopt its feature matching strategy, which defines inliers as the feature pairs subjecting to a global affine transformation. However, for endoscopic videos, we are curious if it is more suitable to cluster features into multiple groups. We can still enforce the same transformation as in SIFT within each group. Such a multi-model idea has been recently examined in the Multi-Affine work, which outperforms Lowe’s SIFT in terms of re-projection error on minimally invasive endoscopic images with manually labelled ground-truth matches of SIFT features. Since their difference lies in matching, the accuracy gain of estimated motion is attributed to the holistic Multi-Affine feature matching algorithm. But, more concretely, the matching criterion and point searching can be the same as those built in SIFT. We argue that the real variation is only the motion model verification. We either enforce a single global motion model or employ a group of multiple local ones. In this paper, we investigate how sensitive the estimated motion is affected by the number of motion models assumed in feature matching. While the sensitivity can be analytically evaluated, we present an empirical analysis in a leaving-one-out cross validation setting without requiring labels of ground-truth matches. Then, the sensitivity is characterized by the variance of a sequence of motion estimates. We present a series of quantitative comparison such as accuracy and variance between Multi-Affine motion models and the global affine model.

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Color-based hybrid reconstruction for endoscopy

Cited by 9 publications

References 21 publications

3D Texture Reconstruction of Abdominal Cavity Based on Monocular Vision SLAM for Minimally Invasive Surgery

3D Texture Reconstruction of Abdominal Cavity Based on Monocular Vision SLAM for Minimally Invasive Surgery

Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment

Is Multi-model Feature Matching Better for Endoscopic Motion Estimation?

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