Automatic deformable registration of histological slides to µCT volume data.

Localizing a histological section in the three-dimensional dataset of a different imaging modality is a challenging 2D-3D registration problem. In the literature, several approaches have been proposed to solve this problem; however, they cannot be considered as fully automatic. Recently, we developed an automatic algorithm that could successfully find the position of a histological section in a micro computed tomography (µCT) volume. For the majority of the datasets, the result of localization corresponded to the manual results. However, for some datasets, the matching µCT slice was off the ground-truth position. Furthermore, elastic distortions, due to histological preparation, could not be accounted for in this framework. In the current study, we introduce two optimization frameworks based on normalized mutual information, which enabled us to accurately register histology slides to volume data. The rigid approach allocated 81 % of histological sections with a median position error of 8.4 µm in jaw bone datasets, and the deformable approach improved registration by 33 µm with respect to the median distance error for four histological slides in the cerebellum dataset.

Can Anatomic Measurements of Stem Anteversion Angle Be Considered as the Functional Anteversion Angle?

BACKGROUND: Stem anteversion angle is important in the combined anteversion theory to avoid implant impingement after total hip arthroplasty (THA). However, anatomic measurements of stem anteversion angle may not represent functional anteversion of the femur if the femur undergoes axial rotation. Herein, the femoral rotational angle (FRA) was measured in supine and standing positions before and after THA to evaluate the difference between anatomic and functional measurements. METHODS: A total of 191 hips (174 patients) treated with THA for osteoarthritis were analyzed in this retrospective, case-controlled study. The FRA was measured as the angle between the posterior condylar line and the line through the bilateral anterior superior iliac spines (positive for external rotation) and was measured preoperatively and postoperatively in supine and standing positions with computed tomography segmentation and landmark localization of the pelvis and the femur followed by intensity-based 2D-3D registration. The number of cases in which the absolute FRA remained <15° in both positions was also calculated. RESULTS: The average ± standard deviation preoperative FRA was 0.3° ± 8.3° in the supine position and -4.5° ± 8.8° during standing; the postoperative FRA was -3.8° ± 9.0° in supine and -14.3° ± 8.3° during standing. There were 134 cases (70%) in which the preoperative absolute FRA remained <15° in both positions while only 85 hips (45%) remained <15°, postoperatively. CONCLUSION: Substantial variability was seen in the FRA, especially during the postoperative period. These results suggest that the anatomic stem anteversion angle may not represent the functional anteversion of the femur.

Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping.

For multi-sensor integrated systems, such as the mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between an optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and a LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for the panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and the LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, with the latter being more reliable.

Intra-operative gaps affect outcome and postoperative kinematics in vivo following cruciate-retaining total knee arthroplasty.

PURPOSE: The following investigation evaluates the effect of intra-operative gaps after posterior cruciate ligament-retaining total knee arthroplasty using two-dimensional/three-dimensional registration and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). METHODS: Patients were divided into two groups according to their 90°-0° component gap changes using a device designed by our laboratory. The wide gap group was defined as more than 3 mm (4.3 ± 0.7 mm), and the narrow gap group was defined as less than 3 mm (1.3 ± 1.3 mm). RESULTS: Under non-WB (weight bearing) conditions, the wide flexion gap group (N = 10) showed a significant anterior displacement of the medial femoral condyle as compared with the narrow flexion gap group (N = 20). Despite no significant differences observed under WB conditions, both femoral condyle positions during flexion were significantly more posterior than during extension. WOMAC of the tight gap group showed worse scores for two functional items demanding knee flexion (bending to floor and getting on/off toilet). CONCLUSION: The large flexion gap could influence the late rollback under non-WB conditions and better WOMAC functional scores in the flexion items. Three to four millimetre laxity at 90°-0° component gaps may be adequate and might be necessary to carry out daily life activities.

Significant effect of the posterior tibial slope on the weight-bearing, midflexion in vivo kinematics after cruciate-retaining total knee arthroplasty.

The purpose of the present study was to compare weight bearing (WB) and non-WB conditions, and to evaluate the effect of the posterior tibial slope (PTS) on the in vivo kinematics of 21 knees after posterior cruciate ligament-retaining total knee arthroplasty during midflexion using 2-dimensional/3-dimensional registration. During WB, medial pivot and bicondylar rollback were observed. During non-WB, both the medial and lateral condyles moved significantly more anteriorly as compared to the WB state. These patients were divided into 2 groups according to their PTS. The large PTS group showed a significant posterior displacement of the medial femoral condyle as compared with the small PTS group, but no significant difference was observed at the lateral femoral condyle during both WB and non-WB. The PTS influenced knee kinematics through gravity (124/125).

In vivo kinematics of three-component mobile-bearing total ankle replacement in rheumatoid ankle with talocalcaneal arthrodesis and spontaneous talocalcaneal fusion.

OBJECTIVE: It is often that patients with rheumatoid arthritis (RA) who require ankle surgeries already have the degeneration of talocalcaneal joints. When talocalcaneal joint was fused, whether operatively or spontaneously, ankle kinematics would be affected. The purpose of this paper was to study in vivo kinematics of mobile-bearing total ankle replacement (TAR) in rheumatoid ankle with concomitant talocalcaneal arthrodesis or with preexisting spontaneous talocalcaneal fusion. METHODS: Thirteen TARs in ten patients with RA, in whom talocalcaneal joints had already been fused spontaneously or surgically, were studied. Fluoroscopic images were obtained while each patient was walking with full weightbearing on the implanted ankle. Thereafter tibio-talar motion was analyzed by 2D/3D registration technique. RESULTS: Average tibio-talar motion was 4.0 ± 5.3° for plantarflexion and 6.6 ± 0.3° for dorsiflexion. Average range of internal/external rotation, inversion/eversion and AP translation was 3.8 ± 1.3°, 2.7 ± 1.0° and 1.6 ± 0.6 mm, respectively. CONCLUSIONS: Mobility of mobile-bearing TAR with talocalcaneal fusion was small during the stance phase of gait, but clinically measured ROM was mostly preserved. The movements of internal/external rotation and AP translation were allowed to a certain degree, but not of inversion/eversion. Even though the movement of inversion/eversion is limited, talocalcaneal arthrodesis could be accompanied with mobile-bearing TAR in rheumatoid ankles.

A grayscale compression method to segment bone structures for 2D-3D registration of setup images in non-coplanar radiotherapy.

Purpose. To evaluate the performance of an automated 2D-3D bone registration algorithm incorporating a grayscale compression method for quantifying patient position errors in non-coplanar radiotherapy.Methods. An automated 2D-3D registration incorporating a grayscale compression method to segment bone structures was proposed. Portal images containing only bone structures (Portalbone) and digitally reconstructed radiographs containing only bone structures (DRRbone) were used for registration. First, the portal image was filtered by a high-pass finite impulse response (FIR) filter. Then the grayscale range of the filtered portal image was compressed. Thresholds were determined based on the difference in gray values of bone structures in the filtered and compressed portal image to obtainPortalbone.Another threshold was applied to generateDRRbonewhen the CT image uses the ray-casting algorithm to generate DRR images. The compression performance was assessed by registering theDRRbonewith thePortalboneobtained by compressing the portal image into various grayscale ranges. The proposed registration method was quantitatively and visually validated using (1) a CT image of an anthropomorphic head phantom and its portal images obtained in different poses and (2) CT images and pre-treatment portal images of 20 patients treated with non-coplanar radiotherapy.Results. Mean absolute registration errors for the best compression grayscale range test were 0.642 mm, 0.574 mm, and 0.643 mm, with calculation times of 50.6 min, 42.2 min, and 49.6 min for grayscale ranges of 0-127, 0-63 and 0-31, respectively. For the accuracy validation (1), the mean absolute registration errors for couch angles 0°, 45°, 90°, 270°, and 315° were 0.694 mm, 0.839 mm, 0.726 mm, 0.833 mm, and 0.873 mm, respectively. Among the six transformation parameters, the translation error in the vertical direction contributed the most to the registration errors. Visual inspection of the patient registration results revealed success in every instance.Conclusions. The implemented grayscale compression method successfully enhances and segments bone structures in portal images, allowing for accurate determination of patient setup errors in non-coplanar radiotherapy.

A Rigorous 2D-3D Registration Method for a High-Speed Bi-Planar Videoradiography Imaging System.

High-speed biplanar videoradiography can derive the dynamic bony translations and rotations required for joint cartilage contact mechanics to provide insights into the mechanical processes and mechanisms of joint degeneration or pathology. A key challenge is the accurate registration of 3D bone models (from MRI or CT scans) with 2D X-ray image pairs. Marker-based or model-based 2D-3D registration can be performed. The former has higher registration accuracy owing to corresponding marker pairs. The latter avoids bead implantation and uses radiograph intensity or features. A rigorous new method based on projection strategy and least-squares estimation that can be used for both methods is proposed and validated by a 3D-printed bone with implanted beads. The results show that it can achieve greater marker-based registration accuracy than the state-of-the-art RSA method. Model-based registration achieved a 3D reconstruction accuracy of 0.79 mm. Systematic offsets between detected edges in the radiographs and their actual position were observed and modeled to improve the reconstruction accuracy to 0.56 mm (tibia) and 0.64 mm (femur). This method is demonstrated on in vivo data, achieving a registration precision of 0.68 mm (tibia) and 0.60 mm (femur). The proposed method allows the determination of accurate 3D kinematic parameters that can be used to calculate joint cartilage contact mechanics.

Fully automatic tracking of native knee kinematics from stereo-radiography with digitally reconstructed radiographs.

Precise measurement of joint-level motion from stereo-radiography facilitates understanding of human movement. Conventional procedures for kinematic tracking require significant manual effort and are time intensive. The current work introduces a method for fully automatic tracking of native knee kinematics from stereo-radiography sequences. The framework consists of three computational steps. First, biplanar radiograph frames are annotated with segmentation maps and key points using a convolutional neural network. Next, initial bone pose estimates are acquired by solving a polynomial optimization problem constructed from annotated key points and anatomic landmarks from digitized models. A semidefinite relaxation is formulated to realize the global minimum of the non-convex problem. Pose estimates are then refined by registering computed tomography-based digitally reconstructed radiographs to masked radiographs. A novel rendering method is also introduced which enables generating digitally reconstructed radiographs from computed tomography scans with inconsistent slice widths. The automatic tracking framework was evaluated with stereo-radiography trials manually tracked with model-image registration, and with frames which capture a synthetic leg phantom. The tracking method produced pose estimates which were consistently similar to manually tracked values; and demonstrated pose errors below 1.0 degree or millimeter for all femur and tibia degrees of freedom in phantom trials. Results indicate the described framework may benefit orthopaedics and biomechanics applications through acceleration of kinematic tracking.

FMB: Dual-view fusion and registration of 2D DSA images and 3D MRA images for neurointerventional-based procedures.

OBJECTIVE: Alignment between preoperative images (high-resolution magnetic resonance imaging, magnetic resonance angiography) and intraoperative medical images (digital subtraction angiography) is currently required in neurointerventional surgery. Treating a lesion is usually guided by a 2D DSA silhouette image. DSA silhouette images increase procedure time and radiation exposure time due to the lack of anatomical information, but information from MRA images can be utilized to compensate for this in order to improve procedure efficiency. In this paper, we abstract this into the problem of relative pose and correspondence between a 3D point and its 2D projection. Multimodal images have a large amount of noise and anomalies that are difficult to resolve using conventional methods. According to our research, there are fewer multimodal fusion methods to perform the full procedure. APPROACH: Therefore, the paper introduces a registration pipeline for multimodal images with fused dual views is presented. Deep learning methods are introduced to accomplish feature extraction of multimodal images to automate the process. Besides, the paper proposes a registration method based on the Factor of Maximum Bounds (FMB). The key insights are to relax the constraints on the lower bound, enhance the constraints on the upper bounds, and mine more local consensus information in the point set using a second perspective to generate accurate pose estimation. MAIN RESULTS: Compared to existing 2D/3D point set registration methods, this method utilizes a different problem formulation, searches the rotation and translation space more efficiently, and improves registration speed. SIGNIFICANCE: Experiments with synthesized and real data show that the proposed method was achieved in accuracy, robustness, and time efficiency.

Scan-Free and Fully Automatic Tracking of Native Knee Anatomy from Dynamic Stereo-Radiography with Statistical Shape and Intensity Models.

Kinematic tracking of native anatomy from stereo-radiography provides a quantitative basis for evaluating human movement. Conventional tracking procedures require significant manual effort and call for acquisition and annotation of subject-specific volumetric medical images. The current work introduces a framework for fully automatic tracking of native knee anatomy from dynamic stereo-radiography which forgoes reliance on volumetric scans. The method consists of three computational steps. First, captured radiographs are annotated with segmentation maps and anatomic landmarks using a convolutional neural network. Next, a non-convex polynomial optimization problem formulated from annotated landmarks is solved to acquire preliminary anatomy and pose estimates. Finally, a global optimization routine is performed for concurrent refinement of anatomy and pose. An objective function is maximized which quantifies similarities between masked radiographs and digitally reconstructed radiographs produced from statistical shape and intensity models. The proposed framework was evaluated against manually tracked trials comprising dynamic activities, and additional frames capturing a static knee phantom. Experiments revealed anatomic surface errors routinely below 1.0 mm in both evaluation cohorts. Median absolute errors of individual bone pose estimates were below 1.0 ∘ or mm for 15 out of 18 degrees of freedom in both evaluation cohorts. Results indicate that accurate pose estimation of native anatomy from stereo-radiography may be performed with significantly reduced manual effort, and without reliance on volumetric scans.

Towards 2D/3D Registration of the Preoperative MRI to Intraoperative Fluoroscopic Images for Visualization of Bone Defects.

Magnetic Resonance Imaging (MRI) is a medical imaging modality that allows for the evaluation of soft-tissue diseases and the assessment of bone quality. Preoperative MRI volumes are used by surgeons to identify defected bones, perform the segmentation of lesions, and generate surgical plans before the surgery. Nevertheless, conventional intraoperative imaging modalities such as fluoroscopy are less sensitive in detecting potential lesions. In this work, we propose a 2D/3D registration pipeline that aims to register preoperative MRI with intraoperative 2D fluoroscopic images. To showcase the feasibility of our approach, we use the core decompression procedure as a surgical example to perform 2D/3D femur registration. The proposed registration pipeline is evaluated using digitally reconstructed radiographs (DRRs) to simulate the intraoperative fluoroscopic images. The resulting transformation from the registration is later used to create overlays of preoperative MRI annotations and planning data to provide intraoperative visual guidance to surgeons. Our results suggest that the proposed registration pipeline is capable of achieving reasonable transformation between MRI and digitally reconstructed fluoroscopic images for intraoperative visualization applications.

2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking.

Two-dimensional (2D)/three-dimensional (3D) registration is critical in clinical applications. However, existing methods suffer from long alignment times and high doses. In this paper, a non-rigid 2D/3D registration method based on deep learning with orthogonal angle projections is proposed. The application can quickly achieve alignment using only two orthogonal angle projections. We tested the method with lungs (with and without tumors) and phantom data. The results show that the Dice and normalized cross-correlations are greater than 0.97 and 0.92, respectively, and the registration time is less than 1.2 seconds. In addition, the proposed model showed the ability to track lung tumors, highlighting the clinical potential of the proposed method.

Visualization in 2D/3D registration matters for assuring technology-assisted image-guided surgery.

PURPOSE: Image-guided navigation and surgical robotics are the next frontiers of minimally invasive surgery. Assuring safety in high-stakes clinical environments is critical for their deployment. 2D/3D registration is an essential, enabling algorithm for most of these systems, as it provides spatial alignment of preoperative data with intraoperative images. While these algorithms have been studied widely, there is a need for verification methods to enable human stakeholders to assess and either approve or reject registration results to ensure safe operation. METHODS: To address the verification problem from the perspective of human perception, we develop novel visualization paradigms and use a sampling method based on approximate posterior distribution to simulate registration offsets. We then conduct a user study with 22 participants to investigate how different visualization paradigms (Neutral, Attention-Guiding, Correspondence-Suggesting) affect human performance in evaluating the simulated 2D/3D registration results using 12 pelvic fluoroscopy images. RESULTS: All three visualization paradigms allow users to perform better than random guessing to differentiate between offsets of varying magnitude. The novel paradigms show better performance than the neutral paradigm when using an absolute threshold to differentiate acceptable and unacceptable registrations (highest accuracy: Correspondence-Suggesting (65.1%), highest F1 score: Attention-Guiding (65.7%)), as well as when using a paradigm-specific threshold for the same discrimination (highest accuracy: Attention-Guiding (70.4%), highest F1 score: Corresponding-Suggesting (65.0%)). CONCLUSION: This study demonstrates that visualization paradigms do affect the human-based assessment of 2D/3D registration errors. However, further exploration is needed to understand this effect better and develop more effective methods to assure accuracy. This research serves as a crucial step toward enhanced surgical autonomy and safety assurance in technology-assisted image-guided surgery.

A Surgical Robotic System for Osteoporotic Hip Augmentation: System Development and Experimental Evaluation.

Minimally-invasive Osteoporotic Hip Augmentation (OHA) by injecting bone cement is a potential treatment option to reduce the risk of hip fracture. This treatment can significantly benefit from computer-assisted planning and execution system to optimize the pattern of cement injection. We present a novel robotic system for the execution of OHA that consists of a 6-DOF robotic arm and integrated drilling and injection component. The minimally-invasive procedure is performed by registering the robot and preoperative images to the surgical scene using multiview image-based 2D/3D registration with no external fiducial attached to the body. The performance of the system is evaluated through experimental sawbone studies as well as cadaveric experiments with intact soft tissues. In the cadaver experiments, distance errors of 3.28mm and 2.64mm for entry and target points and orientation error of 2.30° are calculated. Moreover, the mean surface distance error of 2.13mm with translational error of 4.47mm is reported between injected and planned cement profiles. The experimental results demonstrate the first application of the proposed Robot-Assisted combined Drilling and Injection System (RADIS), incorporating biomechanical planning and intraoperative fiducial-less 2D/3D registration on human cadavers with intact soft tissues.

Mechanistic insights into glenohumeral kinematics derived from positional relationship between the contact path and humeral tuberosity.

Although three-dimensional (3D) glenohumeral (GH) motion has generally been expressed only by rotational elements, its mechanistic details, including GH rotations, remain unknown owing to a lack of geometric investigations. This study aims to investigate the positional relationship between the contact path and humeral tuberosities at the GH joint during arm elevation and to consider the mechanism of GH kinematics. Shoulder kinematics were captured using two-dimensional and 3D single-plane image registration techniques in 15 young healthy subjects during flexion, scaption, and abduction. The glenoid movement relative to the humeral head was calculated to describe the contact path on the humeral head. From the start to 45° of flexion, scaption, and abduction, the glenoid center moved from the anteromedial to the anterior, central, and posterior portions of the humeral head, respectively, as the GH joint rotated externally. From 45° to the maximal elevation for all elevation planes, the glenoid center moved upward to the humeral head and came close to the bicipital groove (BG) at maximal elevation, while the glenoid maintained a constant inclination at 20°-40° relative to the humerus. To investigate this mechanism, the position of humeral tuberosities relative to the glenoid was calculated, and the BG was found to face the supraglenoid tubercle, the attachment site of the long head of biceps (LHB). GH external rotation mainly occurred depending on the elevation planes in the early phase of elevation, and it might be kept constant by the LHB and rotator cuff in the mid- to end range of elevation.

Deep neural network-based synthetic image digital fluoroscopy using digitally reconstructed tomography.

We developed a deep neural network (DNN) to generate X-ray flat panel detector (FPD) images from digitally reconstructed radiographic (DRR) images. FPD and treatment planning CT images were acquired from patients with prostate and head and neck (H&N) malignancies. The DNN parameters were optimized for FPD image synthesis. The synthetic FPD images' features were evaluated to compare to the corresponding ground-truth FPD images using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). The image quality of the synthetic FPD image was also compared with that of the DRR image to understand the performance of our DNN. For the prostate cases, the MAE of the synthetic FPD image was improved (= 0.12 ± 0.02) from that of the input DRR image (= 0.35 ± 0.08). The synthetic FPD image showed higher PSNRs (= 16.81 ± 1.54 dB) than those of the DRR image (= 8.74 ± 1.56 dB), while SSIMs for both images (= 0.69) were almost the same. All metrics for the synthetic FPD images of the H&N cases were improved (MAE 0.08 ± 0.03, PSNR 19.40 ± 2.83 dB, and SSIM 0.80 ± 0.04) compared to those for the DRR image (MAE 0.48 ± 0.11, PSNR 5.74 ± 1.63 dB, and SSIM 0.52 ± 0.09). Our DNN successfully generated FPD images from DRR images. This technique would be useful to increase throughput when images from two different modalities are compared by visual inspection.

A spatial registration method based on 2D-3D registration for an augmented reality spinal surgery navigation system.

BACKGROUND: In order to provide accurate and reliable image guidance for augmented reality (AR) spinal surgery navigation, a spatial registration method has been proposed. METHODS: In the AR spinal surgery navigation system, grayscale-based 2D/3D registration technology has been used to register preoperative computed tomography images with intraoperative X-ray images to complete the spatial registration, and then the fusion of virtual image and real spine has been realised. RESULTS: In the image registration experiment, the success rate of spine model registration was 90%. In the spinal model verification experiment, the surface registration error of the spinal model ranged from 0.361 to 0.612 mm, and the total average surface registration error was 0.501 mm. CONCLUSION: The spatial registration method based on 2D/3D registration technology can be used in AR spinal surgery navigation systems and is highly accurate and minimally invasive.

Shortening image registration time using a deep neural network for patient positional verification in radiotherapy.

We sought to accelerate 2D/3D image registration computation time using image synthesis with a deep neural network (DNN) to generate digitally reconstructed radiographic (DRR) images from X-ray flat panel detector (FPD) images. And we explored the feasibility of using our DNN in the patient setup verification application. Images of the prostate and of the head and neck (H&N) regions were acquired by two oblique X-ray fluoroscopic units and the treatment planning CT. DNN was designed to generate DRR images from the FPD image data. We evaluated the quality of the synthesized DRR images to compare the ground-truth DRR images using the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Image registration accuracy and computation time were evaluated by comparing the 2D-3D image registration algorithm using DRR and FPD image data with DRR and synthesized DRR images. Mean PSNR values were 23.4 ± 3.7 dB and 24.1 ± 3.9 dB for the pelvic and H&N regions, respectively. Mean SSIM values for both cases were also similar (= 0.90). Image registration accuracy was degraded by a mean of 0.43 mm and 0.30°, it was clinically acceptable. Computation time was accelerated by a factor of 0.69. Our DNN successfully generated DRR images from FPD image data, and improved 2D-3D image registration computation time up to 37% in average.

Colonoscopy 3D video dataset with paired depth from 2D-3D registration.

Screening colonoscopy is an important clinical application for several 3D computer vision techniques, including depth estimation, surface reconstruction, and missing region detection. However, the development, evaluation, and comparison of these techniques in real colonoscopy videos remain largely qualitative due to the difficulty of acquiring ground truth data. In this work, we present a Colonoscopy 3D Video Dataset (C3VD) acquired with a high definition clinical colonoscope and high-fidelity colon models for benchmarking computer vision methods in colonoscopy. We introduce a novel multimodal 2D-3D registration technique to register optical video sequences with ground truth rendered views of a known 3D model. The different modalities are registered by transforming optical images to depth maps with a Generative Adversarial Network and aligning edge features with an evolutionary optimizer. This registration method achieves an average translation error of 0.321 millimeters and an average rotation error of 0.159 degrees in simulation experiments where error-free ground truth is available. The method also leverages video information, improving registration accuracy by 55.6% for translation and 60.4% for rotation compared to single frame registration. 22 short video sequences were registered to generate 10,015 total frames with paired ground truth depth, surface normals, optical flow, occlusion, six degree-of-freedom pose, coverage maps, and 3D models. The dataset also includes screening videos acquired by a gastroenterologist with paired ground truth pose and 3D surface models. The dataset and registration source code are available at https://durr.jhu.edu/C3VD.