Search Results (6,815)

Reconstructing 3D indoor scenes from 2D images has always been an important task in computer vision and graphics applications. For indoor scenes, traditional 3D reconstruction methods have problems such as missing surface details, poor reconstruction of large plane textures and uneven illumination areas, and many wrongly reconstructed floating debris noises in the reconstructed models. This paper proposes a 3D reconstruction method for indoor scenes that combines neural radiation field (NeRFs) and signed distance function (SDF) implicit expressions. The volume density of the NeRF is used to provide geometric information for the SDF field, and the learning of geometric shapes and surfaces is strengthened by adding an adaptive normal prior optimization learning process. It not only preserves the high-quality geometric information of the NeRF, but also uses the SDF to generate an explicit mesh with a smooth surface, significantly improving the reconstruction quality of large plane textures and uneven illumination areas in indoor scenes. At the same time, a new regularization term is designed to constrain the weight distribution, making it an ideal unimodal compact distribution, thereby alleviating the problem of uneven density distribution and achieving the effect of floating debris removal in the final model. Experiments show that the 3D reconstruction effect of this paper on ScanNet, Hypersim, and Replica datasets outperforms the state-of-the-art methods. Full article

Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators. Full article

Background: Using two case reports of adult women with moyamoya disease presenting with intracranial hemorrhage from ruptured aneurysms on moyamoya collateral vessels, we aim to demonstrate the potential for effective endovascular treatment navigated by CT angiography, digital subtraction angiography, and flat panel CT. Case 1 Presentation: A 45-year-old female patient with sudden onset of headache, followed by somnolency. CT scan showed a four-ventricle hematocephalus caused by a 27 × 31 × 17 mm hematoma located in the left basal ganglia. Angiography revealed a 3 mm aneurysm on hypertrophic lenticulostriate artery bridging the M1 occlusion. Selective catheterization and distal embolisation with acrylic glue was done. Case 2 Presentation: A 47-year-old woman was admitted for a sudden onset of severe headache, CT scan showed four-ventricle hematocephalus. A 4 mm aneurysm on the collateral vessel–anterior chorioidal artery bridging the closure of the terminal segment of the internal carotid artery was diagnosed as the source of bleeding. Selective catheterization and distal embolisation with acrylic glue was done. Conclusions: Selective embolisation of ruptured aneurysms on moya moya collaterals is a simple, effective, and safe procedure when relevant microcatheters are used with imaging software navigation such as 3D DSA, 3D road map and flat-panel CT. Full article

This study introduces an innovative technique that merges computer vision with topology optimization to advance additive manufacturing. Employing advanced photogrammetry software, we obtain high-resolution images of the design domain, which are then used to develop accurate 3D models through meticulous scanning procedures. These models are transformed into an STL file format and remeshed using an adaptive algorithm within COMSOL 5.3 Multiphysics, facilitated by a custom MATLAB 2023 application. This integration achieves the optimal mesh resolution and precision in analytical assessments. We applied this technique to the design of a concrete pillar for 3D printing, targeting a 75% reduction in volume to improve the material efficiency and structural stability—critical factors for extraterrestrial applications. The design, captured with a 360-degree camera array, guided the MATLAB-based topology optimization process. By combining MATLAB’s optimization algorithms with COMSOL’s meshing and finite element analysis tools, we investigated various material-efficient configurations. The findings reveal a substantial volume reduction, especially in the central region of the design, effectively optimizing material utilization while preserving structural integrity. The optimization algorithm exhibited a swift and stable convergence, reaching near-optimal solutions within approximately 20 iterations. Full article

Article presents a comparison of surface structure study methods, such as atomic force microscopy, scanning and transition electron microscopy in terms of metallic materials 3D-printed using the laser powder bed fusion technique. The main features, advantages, disadvantages of atomic force microscopy as a research method for the LPBF synthesized samples are discussed in the context of hard magnetic material, specifically Nd-Fe-B. The ability to provide qualitative grain structure analysis with the high-resolution images of atomic force microscopy is comprehensively studied. For confirmation good applicability of the above-mentioned method for LPBF sample analysis images of a magnetic domain structure obtained via atomic force microscopy are presented. Thus, the applicability of atomic force microscopy to the quality microstructural investigation of metallic materials obtained by LPBF is demonstrated. Full article

The pyrolysis process of coconut residue and the activated carbon was investigated using thermogravimetric analysis in the range of 25 to 900 °C, with three altered heating rates: 3, 5, and 10 °C/min. The results of the thermal decomposition showed that it occurred in three distinct phases: dehydration, active pyrolysis, and passive pyrolysis. The derivative thermogravimetric analysis indicated that increasing the heating rate led to a shift in the maximum weight loss rate towards higher temperatures. To better understand the kinetics constraints, the Coats–Redfern method was applied to determine the activation energy (Ea) and the frequency factor (A). The activation energies for the pyrolysis process varied between 159.57 and 177.45 kJ/mol for RCR and from 132.62 to 147.1 kJ/mol for ACCR at different heating rates. Additionally, the physical properties of the samples were investigated using techniques like scanning electron microscopy and the Brunauer–Emmett–Teller surface analysis. The findings of the study demonstrated that the activation energies of the activated carbon were lower than those of the original biomass. Furthermore, the activation energy values achieved from the D1–D4 models were considered reliable, indicating that the D model was more suitable compared to other models for describing the pyrolysis process and predicting its kinetics. Full article

Background/Objectives: Nasal morphology is a significant aspect of facial anatomy and is often used for forensic identification and aesthetic surgery. This study aims to compare nasal dimensions based on sex, facial index (FI), and nasal index (NI) using cone-beam computed tomography (CBCT) and 3D modeling. Methods: To observe differences in nasal dimensions by sex and analyze the relationships between facial shapes (FI) and nasal forms (NI), a total of 100 participants (50 males, 50 females) in their 20s were selected from Dankook University Dental Hospital. CBCT scans were performed, and 3D models were created using Mimics software (version 22.0). The measurement items included the alaria distance between (AL), the distance between N (nasion) and SN (subnasale), the distance between N (nasion) and PRN (pronasale), and the distance between SN (subnasale) and PRN (pronasale). A T-test was used for the sex-based analysis of the nasal dimensions, and the facial index- and nasal index-based nasal dimensions were analyzed using a one-way ANOVA with Scherffe’s post hoc test. Additionally, all the statistical analyses were performed using SPSS software (version 23.0). Results: The results indicated that males generally have larger nasal dimensions than females. Additionally, the mesoprosopic facial type (round face) showed the largest nasal dimensions in the FI classification, while the platyrrhine nasal type (broad and short nose) exhibited the largest dimensions in the NI classification. Conclusions: This study demonstrates that the nasal size varies significantly with sex, facial shape, and nasal form. The findings can contribute to forensic identification and provide valuable data for clinical practices in facial reconstruction and nasal surgery. Full article

Background: The aim is to investigate induced higher-order aberrations (HOA)s and astigmatism as a result of non-toric ortho-k lens decentration and utilise artificial intelligence (AI) to predict its magnitude and direction. Methods: Medmont E300 Video topographer was used to scan 249 corneas before and after ortho-k wear. Custom-built MATLAB codes extracted topography data and determined lens decentration from the boundary and midpoint of the central flattened treatment zone (TZ). An evaluation was carried out by conducting Zernike polynomial fittings via a computer-coded digital signal processing procedure. Finally, an AI-based machine learning neural network algorithm was developed to predict the direction and magnitude of TZ decentration. Results: Analysis of the first 21 Zernike polynomial coefficients indicate that the four low-order and four higher-order aberration terms were changed significantly by ortho-k wear. While baseline astigmatism was not correlated with lens decentration (R = 0.09), post-ortho-k astigmatism was moderately correlated with decentration (R = 0.38) and the difference in astigmatism (R = 0.3). Decentration was classified into three groups: ≤0.50 mm, reduced astigmatism by −0.9 ± 1 D; 0.5~1 mm, increased astigmatism by 0.8 ± 0.1 D; >1 mm, increased astigmatism by 2.7 ± 1.6 D and over 50% of lenses were decentred >0.5 mm. For lenses decentred >1 mm, 29.8% of right and 42.7% of left lenses decentred temporal-inferiorly and 13.7% of right and 9.4% of left lenses decentred temporal-superiorly. AI-based prediction successfully identified the decentration direction with accuracies of 70.2% for right and 71.8% for left lenses and predicted the magnitude of decentration with root-mean-square (RMS) of 0.31 mm and 0.25 mm for right and left eyes, respectively. Conclusions: Ortho-k lens decentration is common when fitting non-toric ortho-k lenses, resulting in induced HOAs and astigmatism, with the magnitude being related to the amount of decentration. AI-based algorithms can effectively predict decentration, potentially allowing for better control over ortho-k fitting and, thus, preferred clinical outcomes. Full article

Empyema necessitatis is a rare complication of an untreated or inadequately controlled empyema. We present the case of an 11-year-old female adolescent living in precarious conditions, overcrowding, incomplete vaccinations, irregular dental hygiene, and no significant family or personal medical history. The patient started with symptoms one week prior to her hospitalization, presenting a persistent sporadic dry cough, and was later diagnosed with complicated pneumonia, resulting in the placement of an endopleural tube. Vancomycin (40 mg/kg/day) and ceftriaxone (75 mg/kg/day) were administered. However, the clinical evolution was unfavorable, with fever and respiratory distress, so a right jugular catheter was placed. The CT scan showed a loculated collection that occupied the entire right lung parenchyma and pneumothorax at the right upper lobe level. After four days of treatment, the patient still presented purulent drainage with persistent right pleural effusion syndrome. P. melaninogenica and D. pneumosintes were identified from the purulent collection on the upper right lobe, so the antimicrobial treatment was adapted to a glycopeptide, Teicoplanin, at a weight-based dosing of 6 mg/kg/day and Metronidazole at a weight-based dosing of 30 mg/kg/day. In addition, VAC therapy was used for 26 days with favorable resolution. Full article

Polymer 3D printing has is used in a wide range of applications in the medical field. Polyamide 12 (PA12) is a versatile synthetic polymer that has been used to reconstruct bony defects. Coating its surface with calcium phosphate compounds, such as hydroxyapatite (HA), could enhance its bonding with bone. The aim of this study was to coat 3D-printed polyamide 12 specimens with hydroxyapatite by a simple innovative surface treatment using light-cured resin cement. Polyamide 12 powder was printed by selective laser sintering to produce 80 disc-shaped specimens (15 mm diameter × 1.5 mm thickness). The specimens were divided randomly into two main groups: (1) control group (untreated), where the surface of the specimens was left without any modifications; (2) treated group, where the surface of the specimens was coated with hydroxyapatite by a new method using a light-cured dental cement. The coated specimens were characterised by both Fourier transform infrared spectroscopy (FTIR) and Transmission Electron Microscopy (TEM), (n = 10/test). The control and treated groups were further randomly subdivided into two subgroups according to the immersion in phosphate-buffered saline (PBS). The first subgroup was not immersed in PBS and was left as 3D-printed, while the second subgroup was immersed in PBS for 15 days (n = 10/subgroup). The surfaces of the control and treated specimens were examined using an environmental scanning electron microscope (SEM) and energy dispersive X-ray analysis (EDXA) before and after immersion in PBS. Following the standard American Society for Testing and Materials (ASTM D3359), a cross-cut adhesion test was performed. The results of the FTIR spectroscopy of the coated specimens were confirmed the HA bands. The TEM micrograph revealed agglomerated particles in the coat. The SEM micrographs of the control 3D-printed polyamide 12 specimens illustrated the sintered 3D-printed particles with minimal porosity. Their EDXA revealed the presence of carbon, nitrogen, and oxygen as atomic%: 52.1, 23.8, 24.1 respectively. After immersion in PBS, there were no major changes in the control specimens as detected by SEM and EDXA. The microstructure of the coated specimens showed deposited clusters of calcium and phosphorus on the surface, in addition to carbon, nitrogen, and oxygen, with atomic%: 9.5, 5.9, 7.2, 30.9, and 46.5, respectively. This coat was stable after immersion, as observed by SEM and EDXA. The coat adhesion test demonstrated a stable coat with just a few loose coating flakes (area removed <5%) on the surface of the HA-coated specimens. It could be concluded that the 3D-printed polyamide 12 could be coated with hydroxyapatite using light-cured resin cement. Full article

Spiking neural networks (SNNs) are generating wide attention due to their brain-like simulation capabilities and low energy consumption. Converting artificial neural networks (ANNs) to SNNs provides great advantages, combining the high accuracy of ANNs with the robustness and energy efficiency of SNNs. Existing point clouds processing SNNs have two issues to be solved: first, they lack a specialized surrogate gradient function; second, they are not robust enough to process a real-world dataset. In this work, we present a high-accuracy converted SNN for 3D point cloud processing. Specifically, we first revise and redesign the Spiking X-Convolution module based on the X-transformation. To address the problem of non-differentiable activation function arising from the binary signal from spiking neurons, we propose an effective adjustable surrogate gradient function, which can fit various models well by tuning the parameters. Additionally, we introduce a versatile ANN-to-SNN conversion framework enabling modular transformations. Based on this framework and the spiking X-Convolution module, we design the Spiking PointCNN, a highly efficient converted SNN for processing 3D point clouds. We conduct experiments on the public 3D point cloud datasets ModelNet40 and ScanObjectNN, on which our proposed model achieves excellent accuracy. Code will be available on GitHub. Full article

Effective semantic segmentation of Airborne Laser Scanning (ALS) point clouds is a crucial field of study and influences subsequent point cloud application tasks. Transformer networks have made significant progress in 2D/3D computer vision tasks, exhibiting superior performance. We propose a multilevel geometric feature embedding transformer network (MGFE-T), which aims to fully utilize the three-dimensional structural information carried by point clouds and enhance transformer performance in ALS point cloud semantic segmentation. In the encoding stage, compute the geometric features surrounding tee sampling points at each layer and embed them into the transformer workflow. To ensure that the receptive field of the self-attention mechanism and the geometric computation domain can maintain a consistent scale at each layer, we propose a fixed-radius dilated KNN (FR-DKNN) search method to address the limitation of traditional KNN search methods in considering domain radius. In the decoding stage, we aggregate prediction deviations at each level into a unified loss value, enabling multilevel supervision to improve the network’s feature learning ability at different levels. The MGFE-T network can predict the class label of each point in an end-to-end manner. Experiments were conducted on three widely used benchmark datasets. The results indicate that the MGFE-T network achieves superior OA and mF1 scores on the LASDU and DFC2019 datasets and performs well on the ISPRS dataset with imbalanced classes. Full article

A 6-year-old castrated male mixed dog presented with a rapidly growing mass at the right chest wall two weeks after initial detection. A mesenchymal origin of the malignancy was suspected based on fine-needle aspiration. Computed tomography (CT) revealed that the mass originated from the right chest wall and protruded externally (6.74 × 5.51 × 4.13 cm³) and internally (1.82 × 1.69 × 1.50 cm³). The patient revisited the hospital because of breathing difficulties. Radiography confirmed pleural effusion, and ultrasonography-guided thoracocentesis was performed. The effusion was hemorrhagic, and microscopic evaluation showed no malignant cells. Before surgery, CT without anesthesia was performed to evaluate the status of the patient. The 7–10th ribs were en bloc resected at a 3-cm margin dorsally and ventrally, and two ribs cranially and caudally from the mass. After recovering the collapsed right middle lobe of the lung due to compression from the internal mass with positive-pressure ventilation, a 3D-printed bone model contoured titanium mesh was tied to each covering rib and surrounding muscles using 2-0 blue nylon and closed routinely. The thoracic cavity was successfully reconstructed, and no flail chest was observed. The patient was histo-pathologically diagnosed with extraskeletal osteosarcoma. A CT scan performed 8 months after surgery showed no evident recurrence, metastasis, or implant failure. This is the first case report of chest wall reconstruction using titanium mesh in a dog. The use of a titanium mesh allows for the reconstruction of extensive chest wall defects, regardless of location, without major postoperative complications. Full article

Atrial wall thickness (AWT) is a significant factor in understanding the pathological physiological substrate of atrial fibrillation, with a potentially substantial impact on the outcomes of catheter ablation procedures. Precise measurements of the AWT may provide valuable insights for categorising patients with AF and planning targeted interventions. Objectives: The purpose of this study was to evaluate the characteristics of the left atrium (LA) using non-invasive multidetector computed tomography (MDCT) scans and subsequent three-dimensional (3D) image post-processing using novel software designed to calculate atrial thickness dimensions and mass. Methods: We retrospectively analysed 128 consecutive patients (33.6% females; mean age 55.6 ± 11.2 years) referred for AF ablation (37 with persistent AF and 91 with paroxysmal AF) who underwent preprocedural MDCT. The images were post-processed and analysed using the ADAS software (Galgo Medical), automatically calculating the LA volume and regional wall thickness. In addition, the software employed a regional semi-automatic LA parcellation feature that divided the atrial wall into 12 segments, generating atrial wall thickness (AWT) maps per segment for each patient. Results: This study demonstrated considerable variability in the average thickness of LA walls, with the anterior segments being the thickest across the cohort. Distinct sex-specific differences were observed, with males exhibiting greater anterior and septal wall thickness than females. No significant associations were identified between the average AWT and body mass index, LA volume, or sphericity. Survival analysis conducted over 24 months revealed a meaningful relationship between mean anterior wall thickness and recurrence-free survival, with increased thickness associated with a lower likelihood of AF-free survival. No such relationship was observed for the indexed LA volume. Conclusions: The variability in AWT and its association with recurrence-free survival following AF ablation suggest that AWT should be considered when stratifying patients for AF management and ablation strategies. These findings underscore the need for personalised treatment approaches and further research on the interplay of the structural properties of the left atrium as factors that can serve as important prognostic markers in AF treatment. Full article

Thanks to its highly crystalline structure and excellent thermal, optical, electrical and mechanical properties, carbon and its derivatives are considered the preferred reinforcement material in composites used in many industrial applications, especially in the forest and forest products sector, including oil, gas and aviation. Since hydroxyethyl cellulose (HEC) is a biopolymer, it has poor mechanical and thermal properties. These properties need to be strengthened with various additives. This study aims to improve the thermal and mechanical properties of hydroxyethyl cellulose by preparing hydroxyethyl cellulose/activated carbon (HEC/AC) composite materials. With this study, composites were obtained for the first time and their mechanical properties were examined using a 3D numerical modeling technique. The thermal stability of the prepared composite materials was investigated via thermal gravimetric analysis (TGA). The samples were heated from 30 °C to 750 °C with a heating rate of 10 °C/min under a nitrogen atmosphere and their masses were measured subsequently. The mechanical properties of the composites were investigated via the tensile test. The viscoelastic properties of the composite films were determined with dynamic mechanical thermal analyses (DMTA) and their morphologies were examined with scanning electron microscopy (SEM) images. According to the results, the best F3 sample (films containing 3 wt.% activated carbon) had an elastic modulus of 168.3 MPa, a thermal conductivity value of 0.068 W/mK, the maximum mass loss was at 328.20 °C and the initial storage modulus at 30 °C was 206.13 MPa. It was determined that the hydroxyethyl cellulose composite films containing 3 wt.% activated carbon revealed the optimum results in terms of both thermal conductivity and viscoelastic response and showed that the obtained composite films could be used in industrial applications where thermal conductivity was required. Full article