Mesh Reconstruction Research Articles

Automated 3D Whole-Heart Mesh Reconstruction From 2D Cine MR Slices Using Statistical Shape Model.

Cardiac magnetic resonance (CMR) imaging is the one of the gold standard imaging modalities for the diagnosis and characterization of cardiovascular diseases. The clinical cine protocol of the CMR typically generates high-resolution 2D images of heart tissues in a finite number of separated and independent 2D planes, which are appropriate for the 3D reconstruction of biventricular heart surfaces. However, they are usually inadequate for the whole-heart reconstruction, specifically for both atria. In this regard, the paper presents a novel approach for automated patient-specific 3D whole-heart mesh reconstruction from limited number of 2D cine CMR slices with the help of a statistical shape model (SSM). After extracting the heart contours from 2D cine slices, the SSM is first optimally fitted over the sparse heart contours in 3D space to provide the initial representation of the 3D whole-heart mesh, which is further deformed to minimize the distance from the heart contours for generating the final reconstructed mesh. The reconstruction performance of the proposed approach is evaluated on a cohort of 30 subjects randomly selected from the UK Biobank study, demonstrating the generation of high-quality 3D whole-heart meshes with average contours to surface distance less than the underlying image resolution and the clinical metrics within acceptable ranges reported in previous literature. Clinical Relevance- Automated patient-specific 3D whole-heart mesh reconstruction has numerous applications in car-diac diagnosis and multimodal visualization, including treatment planning, virtual surgery, and biomedical simulations.

Neural dual contouring

We introduce neural dual contouring (NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC). Like traditional DC, it produces exactly one vertex per grid cell and one quad for each grid edge intersection, a natural and efficient structure for reproducing sharp features. However, rather than computing vertex locations and edge crossings with hand-crafted functions that depend directly on difficult-to-obtain surface gradients, NDC uses a neural network to predict them. As a result, NDC can be trained to produce meshes from signed or unsigned distance fields, binary voxel grids, or point clouds (with or without normals); and it can produce open surfaces in cases where the input represents a sheet or partial surface. During experiments with five prominent datasets, we find that NDC, when trained on one of the datasets, generalizes well to the others. Furthermore, NDC provides better surface reconstruction accuracy, feature preservation, output complexity, triangle quality, and inference time in comparison to previous learned (e.g., neural marching cubes, convolutional occupancy networks) and traditional (e.g., Poisson) methods. Code and data are available at https://github.com/czq142857/NDC.

GECM: graph embedded convolution model for hand mesh reconstruction

GECM: graph embedded convolution model for hand mesh reconstruction

Animal pose estimation: A closer look at the state-of-the-art, existing gaps and opportunities

Over the past few years, research on animal pose estimation in computer vision field has grown in many aspects such as 2D and 3D pose estimation, 3D mesh reconstruction, and behavior prediction. Promoted by deep learning, more and more animal pose estimation tools and animal pose datasets have also been made publicly available. However, compared to human pose estimation, which already has high accuracy and high applicability for complex scenes, animal pose estimation is still at a preliminary stage. The huge domain shift between each species, the scarce datasets, and uncooperative research subjects all pose intractable challenges to the development of robust and accurate animal pose estimation algorithms. In this review paper, we summarize the recent (from 2013 to 2021) work in animal pose estimation from computer vision perspective in order to present the state-of-the-art approaches and highlight the challenges they face in this field. We first categorize the various methods of animal pose estimation and present them according to several keywords. Also, we sort and introduce the released annotated image, video, and 3D models of animal poses as well as a promising substitute for real dataset. We also report the performances of the existing algorithms and visualize their results. Finally, we provide an in-depth analysis of the persisting obstacles in this field based on existing work, and offer potential solutions.

A moving finite element method for solving two-dimensional coupled Burgers’ equations at high Reynolds numbers

The coupled Burgers’ equations at high Reynolds numbers usually have sharp gradients or are discontinuous in the solution. Therefore, it is difficult to obtain analytical solutions. This paper aims to use the moving finite element method proposed by Li et al. (2001) to get stable and high-precision numerical solutions for the coupled Burgers’ equations at high Reynolds numbers. The method decouples the mesh equation and partial differential equation (PDE) into two unrelated parts, mesh reconstruction and PDE solver. The mesh reconstruction constructs the harmonic mapping between the physical and logical domains through an iterative method so that the mesh structure maintains harmonics after multiple numerical integrations. We compute three classic numerical examples. Numerical results show that the moving finite element method effectively solve the coupled Burgers’ equations at high Reynolds numbers, obtain stable numerical results, and achieve higher numerical accuracy. During the evolution of the solution, the mesh is always concentrated in the position where the solution has sharp gradients.

Single‐image human mesh reconstruction by parallel spatial feature aggregation

AbstractRecovering human mesh from a single image with natural postures is a challenging task in human modeling and animation. Model‐free methods regress the mesh vertices from the input image directly to avoid the 6‐DoF human joint extraction from the 2D image. However, the missing of the global information in spatial feature aggregation of the existing GNNs may result in the undesired deformity and inaccuracy of the recovered human mesh. To address this issue, we propose a parallel‐aggregating network with a novelly designed global layer for spatial feature extracting from random walk normalized matrix. Moreover, the coarse body mesh (head, hand, foot, etc.) provided by the coarsening network can add the human characteristic to the mesh. The local and global spatial features are aggregated to update vertice coordinates following an iterative, coarse‐to‐fine process to obtain an accurate and smooth human mesh. Experiments validated the effectiveness and robustness of the proposed approaches for single‐image human mesh recovery.

Management of Post COVID 19 Mucormycosis with Osteomyelitis Management of Post COVID 19 Mucormycosis with Osteomyelitis in The Maxilla, New Combined Consequences of The Pandemic. in The Maxilla, New Combined Consequences of The Pandemic. (Case Report).

Aim: This case report aims to demonstrate among health providers the importance of timely surgical and prosthetic management procedures of patients who suffered from the newly combined clinical entity of post COVID 19 mucormycosis and osteomyelitis of the palate. Materials and methods: Elaborate advanced diagnosis of the patients including CBCT, MRI, CT, Pathology examination, 3Ds printing reconstruction of the whole skull and digital design of reconstructive mesh that support the prosthesis were carried out. The patients undergone modified infrastructure maxillectomy and immediate prosthetic obturation of the palatal defect that was replaced later by a delayed obturator. Results: Surgical eradication of the necrotic bone and subsequent immediate obturation of the defect minimized morbidly and helped in improving the quality of life of these patients. Conclusion: Early diagnosis of this potentially fatal disease and reach-out a treatment plan immediately, is of prime importance in reducing the morbidly and mortality rate and enhance speech and eating in the two patients reported. It also decreases the psychological impact of this radical surgery.

Classification of Ancient Buddhist Architecture in Multi-Cultural Context Based on Local Feature Learning

Ancient Buddhist architecture plays an important role in the development of Chinese architectural culture. Under the background of multiculturalism, the ancient Buddhist architectural style has also been influenced to varying degrees. In order to realize automatic classification of ancient Buddhist architecture under multi-cultural background, this paper proposes an automatic classification algorithm based on local feature learning. Firstly, the ancient Buddhist architecture images are gridded, so that the backbone network can obtain relatively flat ancient Buddhist architecture image blocks. At the same time, the backbone network can learn more local details. Then, the grid reconstruction module is designed to strengthen the connection between the features of each block and highlight the distinguishing detail features. The accuracy of ancient Buddhist architecture classification can be effectively improved through image meshing and mesh reconstruction. Experiment and analysis are carried out by using the dataset of ancient Buddhist architecture images on the Internet. Experimental results show that the proposed algorithm has better recognition accuracy and robustness than other comparison algorithms.

Defect identification in heat transfer problems using boundary data

The identification of internal defects has been widely focused and employed in the field of nondestructive evaluation (NDE), whereas its application in inverse heat transfer is often restricted by connectedness. Thus, in this paper, a novel identification method of multiple defects based on the parametric level set method (PLSM) and the Method of Moving Asymptotes (MMA) is proposed, where the connectedness limitation is released. In PLSM, the compactly supported radial basis functions (CSRBFs) are utilized to decouple the material related level set function, which endows the PLSM to be capable of forming high curvature portions and capturing local details of the defects. After that, the evolution of guessed level set can be tracked by updating the time-dependent expansion coefficients. Then, a least-square problem is defined to find the optimal coefficients that correspond to the actual defects. To obtain the controllable convergence and stabilize the solving of the ill-posed optimization problem, the MMA is employed to minimize the objective function. Furthermore, no requirement for the mesh reconstruction after the usage of ersatz material model, one also renders the sensitivity prone to compute. The efficiency and accuracy of the proposed method are further validated in several numerical examples by discussing the identification of single and multiple defects.

Abdominal Wall Reconstruction Using Omental Flap with Mesh Repair following Resection of Aggressive Abdominal Wall Neoplasms

Background: Resection of large anterior abdominal wall tumors causes large full-thickness abdominal wall defects, and the repair of these defects remains a challenging point. Methods: Between July 2016 and February 2021, we retrospectively reviewed the internal database registry of the Oncology Center, Mansoura University (OCMU), Egypt, for patients with large abdominal wall defects after abdominal wall tumors resection and repair with omental flaps and synthetic polypropylene (PP) mesh. Thirty-two patients met the inclusion criteria. They were analyzed for demographics and operative data including defect size, mesh size, intra-abdominal tumor extension, and postoperative outcomes and complications. Results: Thirty-Two patients with abdominal wall neoplasm underwent local resection in our center and the defect was closed with an omental flap and PP mesh. The mean operative time was 143.75 ± 30.77 min. The mean size of the abdominal wall defect was 50.8 cm² (range: 25–90 cm²). The meshes used in reconstruction had a mean size of 89.5 cm² (range: 55–130 cm²). The median follow-up period of the patients was 13.5 months (range: 5–54 months). Postoperative complications included infection (n = 4 cases), seroma (n = 2 cases), hematoma (n = 1 case), and abnormal sensation (n = 5 cases). Tumor recurrence was reported in 2 cases, and no cases developed incisional hernia during the follow-up period. Conclusion: Immediate use of omental flap with synthetic PP mesh for reconstruction of abdominal wall defects is a feasible technique and has avoided the complications associated with the use of synthetic mesh alone.

Ten-year surgical complications and mesh erosion of transvaginal Elevate™ mesh for management of pelvic organ prolapse

This study aims to review the surgical complications and mesh erosion with a cohort of patients who underwent Elevate™ mesh repair. This was a single-centre, retrospective study of women undergoing Elevate™ mesh reconstruction between 2006 and 2016. Women who presented with symptomatic prolapse of stage II or above were enrolled. The study outcomes were intra-operative and peri-operative complications (including mesh complications) at six weeks, one year and five years reporting with a standardised communicable method—the International Urogynecological Association (IUGA)/International Continence Society (ICS) scale and the Clavien and Dindo Classification. A total number of 350 women had Elevate™ mesh insertion. The mesh exposure rate was 1.1% and the surgical complication rate in our series was low and self-limiting (2% were Dindo grade 3). With the low morbidity and mesh-related complications, Elevate™ mesh should be at least kept as an alternative treatment of pelvic organ prolapse. Impact statement What is already known on this subject? Transvaginal mesh repair surgery was popular to treat advanced pelvic organ prolapse 10 years ago. However, after announcement of the Food and Drug Administration (FDA) public notification regarding mesh-related complications, particularly mesh exposure, use of transvaginal mesh and its safety have been scrutinised. Despite the increasing challenging and difficult medico-legal climate, several studies on second generation mesh kits, such as Elevate™ have shown favourable outcomes with low complication rates including our group. What do the results of this study add? In this paper, the surgical complications and mesh erosion of Transvaginal mesh was reported in a quantified communicable method—the International Urogynecological Association (IUGA)/International Continence Society (ICS) scale and the Clavien and Dindo Classification. This is significant because it is the largest case studies from a single centre with 350 patients. What are the implications of these findings for clinical practice and/or further research? This study well demonstrated using a standardised way to classify mesh exposure and peri-operative complications which is lacking in the past, while majority studies only focus to discuss on surgical outcome instead of complications. This may make a spark for future research analysis.

Fluid–Structure Interaction Modeling of Ascending Thoracic Aortic Aneurysms in SimVascular

Ascending Thoracic Aortic Aneurysm (ATAA) is a permanent dilatation of the aorta which is usually related to tissue degeneration, hemodynamic conditions, lifestyle, environmental and genetic factors. As the mechanical conditions can become critical in a dilated aorta, a patient-specific computational model can be very useful to assist clinical decisions in the management of ATAAs. In this article, we model the biomechanical conditions of ATAA by performing Fluid–Structure Interaction (FSI) simulations in the SimVascular open-source software package. The patient-specific geometric model is reconstructed from Computed Tomography scan (CT). The numerical implementation takes into account patient-specific outlet conditions and a temporal flow variation at the model inlet. We performed a mesh convergence analysis on a new mesh reconstruction method in SimVascular and showed that it can significantly reduce the computational cost without impacting the accuracy.

Iterative offset-based method for reconstructing a mesh model from the point cloud of a pig

In the livestock industry, assuring the quality of livestock products is as important as reducing breeding costs. The quality of livestock is generally evaluated based on their body form and weight. More specifically, a series of measurements using depth cameras or laser scanners are conducted to obtain 3D data in the form of a point cloud, in an attempt to monitor and determine the breeding status of livestock. In previous studies on livestock weight estimation, major body dimensions were first calculated using point cloud data and based on the results, livestock weight was estimated using weight estimation formulas. These approaches, however, failed to consider the entire body shape of the livestock over the course of weight estimation. In the present study, a mesh reconstruction method for the point clouds of pigs, and especially those with a large amount of noise or missing data points, was proposed as a means of pig weight estimation. The basic principles of the mesh-model reconstruction process are as follows. First, a primitive shape is created in the form of a mesh and the travel distance for each node contained in the mesh model is calculated. The nodes are moved according to the calculation, and triangular element division and smoothing are performed for quality improvement. Iterations of node travel and quality improvement processes are conducted to reshape the mesh model to fit the shape of the corresponding point cloud. To verify the proposed method, point clouds were obtained by scanning actual pigs and mesh models were automatically reconstructed to fit the obtained point clouds. The results showed that the maximum distance between the point cloud with noise removed and the corresponding data points of the mesh model, was within 3.12 mm for all cases. These results confirmed that the reconstructed mesh model provided a satisfactory performance.

Application of digital modeling and three-dimensional printing of titanium mesh for reconstruction of thyroid cartilage in partial laryngectomy

Background Digital modeling and three-dimensional (3D) printing techniques have been used to assist the resection of the laryngeal lesions and repair the remnant larynx in glottic cancer patients with anterior commissure involvement. Aims/objectives To evaluate the feasibility of digital modeling and 3D printing of titanium mesh for thyroid cartilage reconstruction in partial laryngectomy, and compare the advantages and disadvantages with crico-hyoido-epiglottopexy (CHEP). Material and methods Forty-four glottic cancer patients with anterior commissure involvement were randomly assigned into group A and group B. The digital modeling and 3 D printing were used for patients in group A, and patients in group B underwent the modified CHEP. Results In group A, 10 patients underwent tracheotomy and tracheal tube was removed after 2 weeks. All the patients were discharged within 10 d after surgery, and the majority of them had a satisfactory level of pronunciation. In group B, the majority of the patients were discharged 2 − 3 weeks after surgery with a moderate level of pronunciation. Conclusions and significance The proposed surgical method, employing digital modeling and 3D printing to facilitate resection of laryngeal lesions and reconstruction of residual larynx, exhibited to be beneficial for accurate reconstruction of thyroid cartilage and soft tissues.

VoxDMRN: a voxelwise deep max-pooling residual network for bioluminescence tomography reconstruction.

Bioluminescence tomography (BLT) has extensive applications in preclinical studies for cancer research and drug development. However, the spatial resolution of BLT is inadequate because the numerical methods are limited for solving the physical models of photon propagation and the restriction of using tetrahedral meshes for reconstruction. We conducted a series of theoretical derivations and divided the BLT reconstruction process into two steps: feature extraction and nonlinear mapping. Inspired by deep learning, a voxelwise deep max-pooling residual network (VoxDMRN) is proposed to establish the nonlinear relationship between the internal bioluminescent source and surface boundary density to improve the spatial resolution in BLT reconstruction. The numerical simulation and in vivo experiments both demonstrated that VoxDMRN greatly improves the reconstruction performance regarding location accuracy, shape recovery capability, dual-source resolution, robustness, and in vivo practicability.

GeoRec: Geometry-enhanced semantic 3D reconstruction of RGB-D indoor scenes

Semantic indoor 3D modeling with multi-task deep neural networks is an efficient and low-cost way for reconstructing an indoor scene with geometrically complete room structure and semantic 3D individuals. Challenged by the complexity and clutter of indoor scenarios, the semantic reconstruction quality of current methods is still limited by the insufficient exploration and learning of 3D geometry information. To this end, this paper proposes an end-to-end multi-task neural network for geometry-enhanced semantic 3D reconstruction of RGB-D indoor scenes (termed as GeoRec). In the proposed GeoRec, we build a geometry extractor that can effectively learn geometry-enhanced feature representation from depth data, to improve the estimation accuracy of layout, camera pose and 3D object bounding boxes. We also introduce a novel object mesh generator that strengthens the reconstruction robustness of GeoRec to indoor occlusion with geometry-enhanced implicit shape embedding. With the parsed scene semantics and geometries, the proposed GeoRec reconstructs an indoor scene by placing reconstructed object mesh models with 3D object detection results in the estimated layout cuboid. Extensive experiments conducted on two benchmark datasets show that the proposed GeoRec yields outstanding performance with 5.19×10-3 mean chamfer distance error for object reconstruction on the challenging Pix3D dataset, 70.45% mAP for 3D object detection and 77.1% 3D mIoU for layout estimation on the commonly-used SUN RGB-D dataset. Especially, the mesh reconstruction sub-network of GeoRec trained on Pix3D can be directly transferred to SUN RGB-D without any fine-tuning, manifesting a high generalization ability.

The Effect of Biological Scaffold (Biodesign) in Postmastectomy Direct-to-Implant Breast Reconstruction: A 5-Year Single-Institution Experience.

Direct-to-implant (DTI) breast reconstruction is one of the immediate implant-based breast reconstruction methods. If the amount of soft tissue (eg, muscle or fascia) is insufficient to completely cover the implant, biological scaffold or acellular dermal matrix can be safely used for implant coverage. In this study, we used an acellular porcine small intestinal submucosa (SIS) mesh (Biodesign; Cook Medical Inc, Bloomington, IN) for DTI reconstruction to explore the impact of its use on breast reconstruction results. We retrospectively assessed cases involving DTI reconstruction at Taipei Veterans General Hospital from 2015 to 2019. Women, 18 years or older, who underwent immediate DTI reconstruction after mastectomy were included in the study. Mastectomy may have been performed because of therapeutic or prophylactic reasons. Patients who did and did not use SIS mesh for reconstruction were studied separately, and the 2 groups were compared in terms of clinical outcomes and complications. The validated, self-administered BREAST-Q Reconstruction Module version 2.0 survey was used to evaluate health-related quality of life and satisfaction among patients who underwent breast reconstruction. A total of 30 DTI breast reconstructions were enrolled. The mean age was 49.2 years, and the mean body mass index was 22.3 kg/m2. The mean postoperative follow-up duration was 17.1 months. Nipple-sparing mastectomy was performed for 26 cases (86.7%), and DTI breast reconstructions using SIS mesh for implant coverage were done in 14 cases (46.7%). The overall complication rate was 53.3% in 30 reconstructions, with nipple complications being the most common complication. The non-SIS and SIS-using groups had a similar overall complication rate postoperatively. As for the quality-of-life assessment, the SIS group obtained a higher score on BREAST-Q than those for whom SIS was not used. Porcine SIS mesh might be a safe and effective alternative to biological scaffolds in immediate 1-stage implant-based breast reconstruction to improve the quality of life after surgery.

Research on 3D Defect Information Management of Drainage Pipeline Based on BIM

With the age of pipeline and increase in the volume of urban sewage, the pipeline has different degrees of defects, which can cause safety problems such as road collapse and urban flooding. The service life of drainage pipes is closely related to daily maintenance and inspection, so it is very important to inspect the defects and monitor the operation of drainage pipes regularly. However, the existing research lacks quantitative detection and intelligent management of pipeline defect information. Therefore, the depth camera is used as the sensor to quantitatively detect the volume and area of the pit on the concrete pipe, and a defect information management platform is constructed in this paper. Firstly, combined BIM model with 3D point cloud, this paper proposes a 3D defect information management platform of drainage pipeline. Then, the depth camera is used to collect the damage data and preprocess the data, and a method for calculating the damage volume and surface area of drainage pipeline based on 3D mesh reconstruction of the defect point cloud is proposed. The verification experiment results show that the error between the quantized volume and the real volume is mostly within 10%, and the maximum error is 17.54%, indicating high accuracy. The drainage pipeline information model is created. Finally, the data is uploaded to the information management platform to realize the visualization and informatization of pipeline defects and the later operation and maintenance requirements of the pipeline.

Accuracy of globe-sparing orbital reconstruction using individually bent titanium mesh: A comparative study

Accurate reconstruction of orbital and midfacial defects following extensive globe-sparing maxillectomy is challenging, due to the complex anatomy of facial skeleton. The aim of this study is to evaluate the outcomes of individually bent titanium mesh in navigation-assisted reconstruction of post-ablative orbits in comparison with that without intraoperative navigation. Forty-one patients undergone globe-sparing maxillectomy and orbital floor reconstruction using individually bent titanium mesh with or without intraoperative navigation were assessed. Pre- and postoperative orbital projection and volume measurements were performed on both orbits. The unaffected orbit was used as a control for comparison. True-to-original orbital reconstruction was achieved in this study. The average difference of globe projection and orbital volume between unaffected and reconstructed orbits was 0.8±0.5mm and 0.9±1.2cm3, respectively, in navigation-assisted group. In non-navigation-assisted group, the average difference of globe projection and orbital volume of unaffected and reconstructed orbit was 0.7±0.5mm and 1.3±1.3cm3, respectively. There was no statistical significance in mean differences between unaffected and affected globe projection (P=0.744) and orbital volume (P=0.677) in both groups. There was also no significant difference observed when comparing the mean differences between pre- and postoperative globe projection (P=0.659) and orbital volume (P=0.582) in both groups. While intraoperative navigation system was shown to be effective in orbital reconstruction in the past decade, equal satisfactory post-ablative orbital reconstruction can be achieved with individually bent titanium mesh with or without intraoperative navigation.

Predictors of Clinical Outcome in Women with Pelvic Organ Prolapse Who Underwent Transvaginal Mesh Reconstruction Surgery.

Background and Objectives: To identify the predictors of clinical outcomes in women with pelvic organ prolapse (POP) who underwent transvaginal reconstruction surgery, especially with transobturator mesh fixation or sacrospinous mesh fixation. Materials and Methods: All women with POP who underwent transvaginal reconstruction surgery, especially with transobturator mesh fixation or sacrospinous mesh fixation, were reviewed. Results: Between January 2011 and May 2019, a total of 206 consecutive women were reviewed, including 68 women receiving POP reconstruction with transobturator mesh fixation and 138 women who underwent POP reconstruction with sacrospinous mesh fixation. The least experienced surgeon (hazard ratio = 804.6) and advanced stage of cystocele (hazard ratio = 8.80) were the predictors of POP recurrence, especially those women with stage 4 of cystocele. Young age (hazard ratio = 0.94) was a predictor for mesh extrusion, especially those women with age ≤67 years. Follow-up interval (odds ratio = 1.03, p = 0.02) was also an independent predictor of mesh extrusion. High maximum flow rate (Qmax, hazard ratio = 1.03) was the sole predictor of postoperative stress urinary incontinence, especially those women with Qmax ≥19.2 mL/s. Preoperative overactive bladder syndrome (hazard ratio = 3.22) were a predictor for postoperative overactive bladder syndrome. In addition, overactive bladder syndrome rate improved after surgery in the sacrospinous group (p = 0.0001). Voiding dysfunction rates improved after surgery in both sacrospinous and transobturator groups. Conclusions: Predictors of clinical outcome in women who underwent transvaginal POP mesh reconstruction are identified. The findings can serve as a guide for preoperative consultation of similar procedures.