Towards Live Monocular 3D Laparoscopy Using Shading and Specularity Information

Image-Based 3D Reconstruction in Laparoscopy: A Review Focusing on the Quantitative Evaluation by Applying the Reconstruction Error

Image-based 3D reconstruction enables laparoscopic applications as image-guided navigation and (autonomous) robot-assisted interventions, which require a high accuracy. The review's purpose is to present the accuracy of different techniques to label the most promising. A systematic literature search with PubMed and google scholar from 2015 to 2023 was applied by following the framework of "Review articles: purpose, process, and structure". Articles were considered when presenting a quantitative evaluation (root mean squared error and mean absolute error) of the reconstruction error (Euclidean distance between real and reconstructed surface). The search provides 995 articles, which were reduced to 48 articles after applying exclusion criteria. From these, a reconstruction error data set could be generated for the techniques of stereo vision, Shape-from-Motion, Simultaneous Localization and Mapping, deep-learning, and structured light. The reconstruction error varies from below one millimeter to higher than ten millimeters-with deep-learning and Simultaneous Localization and Mapping delivering the best results under intraoperative conditions. The high variance emerges from different experimental conditions. In conclusion, submillimeter accuracy is challenging, but promising image-based 3D reconstruction techniques could be identified. For future research, we recommend computing the reconstruction error for comparison purposes and use ex/in vivo organs as reference objects for realistic experiments.

A Monocular Variable Magnifications 3D Laparoscope System Using Double Liquid Lenses

During minimal invasive surgery (MIS), the laparoscope only provides a single viewpoint to the surgeon, leaving a lack of 3D perception. Many works have been proposed to obtain depth and 3D reconstruction by designing a new optical structure or by depending on the camera pose and image sequences. Most of these works modify the structure of the conventional laparoscopes and cannot provide 3D reconstruction of different magnification views. In this study, we propose a laparoscopic system based on double liquid lenses, which provide doctors with variable magnification rates, near observation, and real-time monocular 3D reconstruction. Our system composes of an optical structure that can obtain auto magnification change and autofocus without any physically moving element, and a deep learning network based on the Depth from Defocus (DFD) method, trained to suit inconsistent camera intrinsic situations and estimate depth from images of different focal lengths. The optical structure is portable and can be mounted on conventional laparoscopes. The depth estimation network estimates depth in real-time from monocular images of different focal lengths and magnification rates. Experiments show that our system provides a 0.68-1.44x zoom rate and can estimate depth from different magnification rates at 6fps. Monocular 3D reconstruction reaches at least 6mm accuracy. The system also provides a clear view even under 1mm close working distance. Ex-vivo experiments and implementation on clinical images prove that our system provides doctors with a magnified clear view of the lesion, as well as quick monocular depth perception during laparoscopy, which help surgeons get better detection and size diagnosis of the abdomen during laparoscope surgeries.

Colonoscopic 3D reconstruction by tubular non-rigid structure-from-motion

PURPOSE

The visual examination of colonoscopic images fails to extract precise geometric information of the colonic surface. Reconstructing the 3D surface of the colon from colonoscopic image sequences may thus add valuable clinical information. We address this problem of extracting precise spatio-temporal 3D structure information from colonoscopic images.

METHODS

Using just the intrinsically calibrated monocular image stream, we develop a technique to compute the depth of certain feature points that have been tracked across images. Our method uses the prior knowledge of an approximate geometry of the colon, called the (TTP). It works by fitting a deformable cylindrical model to points reconstructed independently by non-rigid structure-from-motion (NRSfM), compromising between the data term and a novel tubular smoothing prior. Our method represents the first method ever to exploit a very weak topological prior to improve NRSfM. As such, it lies in-between standard NRSfM, which does not use a topological prior beyond the mere plane, and shape-from-template (SfT), which uses a very strong prior as a full deformable 3D object model.

RESULTS

We validate our method on both synthetic images of tubular structures and real colonoscopic data. Our method improves the results obtained by existing NRSfM methods by 71.74% on average on synthetic data and succeeds in obtaining 3D reconstruction from a real colonoscopic sequence defeating the existing methods.

CONCLUSION

Colonoscopic 3D reconstruction is a difficult problem, which is yet unresolved by the existing methods from computer vision. Our proposed dedicated NRSfM method and experiments show that the visual motion might be the right visual cue to use in colonoscopy.

Light modelling and calibration in laparoscopy

PURPOSE

A better understanding of photometry in laparoscopic images can increase the reliability of computer-assisted surgery applications. Photometry requires modelling illumination, tissue reflectance and camera response. There exists a large variety of light models, but no systematic and reproducible evaluation. We present a review of light models in laparoscopic surgery, a unified calibration approach, an evaluation methodology, and a practical use of photometry.

METHOD

We use images of a calibration checkerboard to calibrate the light models. We then use these models in a proposed dense stereo algorithm exploiting the shading and simultaneously extracting the tissue albedo, which we call dense shading stereo. The approach works with a broad range of light models, giving us a way to test their respective merits.

RESULTS

We show that overly complex light models are usually not needed and that the light source position must be calibrated. We also show that dense shading stereo outperforms existing methods, in terms of both geometric and photometric errors, and achieves sub-millimeter accuracy.

CONCLUSION

This work demonstrates the importance of careful light modelling and calibration for computer-assisted surgical applications. It gives guidelines on choosing the best performing light model.

A Real-Time 3D Laparoscopic Imaging System: Design, Method, and Validation

OBJECTIVE

This paper aims to propose a 3D laparoscopic imaging system that can realize dense 3D reconstruction in real time.

METHODS

Based on the active stereo technique which yields high-density, accurate and robust 3D reconstruction by combining structured light and stereo vision, we design a laparoscopic system consisting of two image feedback channels and one pattern projection channel. Remote high-speed image acquisition and pattern generation lay the foundation for the real-time dense 3D surface reconstruction and enable the miniaturization of the laparoscopic probe. To enhance the reconstruction efficiency and accuracy, we propose a novel active stereo method by which the dense 3D point cloud is obtained using only five patterns, while most existing multiple-shot structured light techniques require [Formula: see text] patterns. In our method, dual-frequency phase-shifting fringes are utilized to uniquely encode the pixels of the measured targets, and a dual-codeword matching scheme is developed to simplify the matching procedure and achieve high-precision reconstruction.

RESULTS

Compared with the existing structured light techniques, the proposed method shows better real-time efficiency and accuracy in both quantitative and qualitative ways. Ex-vivo experiments demonstrate the robustness of the proposed method to different biological organs and the effectiveness to lesions and deformations of the organs. Feasibility of the proposed system for real-time dense 3D reconstruction is verified in dynamic experiments. According to the experimental results, the system acquires 3D point clouds with a speed of 12 frames per second. Each frame contains more than 40,000 points, and the average errors tested on standard objects are less than 0.2 mm.

SIGNIFICANCE

This paper provides a new real-time dense 3D reconstruction method for 3D laparoscopic imaging. The established prototype system has shown good performance in reconstructing surface of biological tissues.

Designing a New Endoscope for Panoramic-View with Focus-Area 3D-Vision in Minimally Invasive Surgery

Purpose

The minimally invasive surgery (MIS) has shown advantages when compared to traditional surgery. However, there are two major challenges in the MIS technique: the limited field of view (FOV) and the lack of depth perception provided by the standard monocular endoscope. Therefore, in this study, we proposed a New Endoscope for Panoramic-View with Focus-Area 3D-Vision (3DMISPE) in order to provide surgeons with a broad view field and 3D images in the surgical area for real-time display.

Method

The proposed system consisted of two endoscopic cameras fixed to each other. Compared to our previous study, the proposed algorithm for the stitching videos was novel. This proposed stitching algorithm was based on the stereo vision synthesis theory. Thus, this new method can support 3D reconstruction and image stitching at the same time. Moreover, our approach employed the same functions on reconstructing 3D surface images by calculating the overlap region’s disparity and performing image stitching with the two-view images from both the cameras.

Results

The experimental results demonstrated that the proposed method can combine two endoscope’s FOV into one wider FOV. In addition, the part in the overlap region could also be synthesized for a 3D display to provide more information about depth and distance, with an error of about 1 mm. In the proposed system, the performance could achieve a frame rate of up to 11.3 fps on a single Intel i5-4590 CPU computer and 17.6 fps on a computer with an additional GTX1060 Nvidia GeForce GPU. Furthermore, the proposed stitching method in this study could be made 1.4 times after when compared to that in our previous report. Besides, our method also improved stitched image quality by significantly reducing the alignment errors or “ghosting” when compared to the SURF-based stitching method employed in our previous study.

Conclusion

The proposed system can provide a more efficient way for the doctors with a broad area of view while still providing a 3D surface image in real-time applications. Our system give promises to improve existing limitations in laparoscopic surgery such as the limited FOV and the lack of depth perception.

Fast and accurate vision-based stereo reconstruction and motion estimation for image-guided liver surgery

Image-guided liver surgery aims to enhance the precision of resection and ablation by providing fast localisation of tumours and adjacent complex vasculature to improve oncologic outcome. This Letter presents a novel end-to-end solution for fast stereo reconstruction and motion estimation that demonstrates high accuracy with phantom and clinical data. The authors’ computationally efficient coarse-to-fine (CTF) stereo approach facilitates liver imaging by accounting for low texture regions, enabling precise three-dimensional (3D) boundary recovery through the use of adaptive windows and utilising a robust 3D motion estimator to reject spurious data. To the best of their knowledge, theirs is the only adaptive CTF matching approach to reconstruction and motion estimation that registers time series of reconstructions to a single key frame for registration to a volumetric computed tomography scan. The system is evaluated empirically in controlled laboratory experiments with a liver phantom and motorised stages for precise quantitative evaluation. Additional evaluation is provided through testing with patient data during liver resection.

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

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.

An augmented reality framework for soft tissue surgery

Augmented reality for soft tissue laparoscopic surgery is a growing topic of interest in the medical community and has potential application in intra-operative planning and image guidance. Delivery of such systems to the operating room remains complex with theoretical challenges related to tissue deformation and the practical limitations of imaging equipment. Current research in this area generally only solves part of the registration pipeline or relies on fiducials, manual model alignment or assumes that tissue is static. This paper proposes a novel augmented reality framework for intra-operative planning: the approach co-registers pre-operative CT with stereo laparoscopic images using cone beam CT and fluoroscopy as bridging modalities. It does not require fiducials or manual alignment and compensates for tissue deformation from insufflation and respiration while allowing the laparoscope to be navigated. The paper's theoretical and practical contributions are validated using simulated, phantom, ex vivo, in vivo and non medical data.