3D Approach Research Articles

Knee cartilage MR images segmentation based on multi-dimensional hybrid convolutional neural network

Accurate segmentation of knee cartilage in MR images is crucial for early diagnosis and treatment of knee conditions. Manual segmentation is time-consuming, leading researchers to explore automatic deep learning methods. However, the choice between 2D and 3D networks for organ segmentation remains debated. In this paper, we propose a hybrid 2D and 3D deep neural network approach, named UVNet, which combines the strengths of both techniques to enhance segmentation performance. Within this network structure, the 3D segmentation network serves as the backbone for feature extraction, while the 2D segmentation network functions as an information supplement network. Local and global MIP images are generated by employing various maximum intensity projection modes of knee MRI volumes as input for the information supplement network. By constructing a local and global MIP feature fusion module, the supplementary information obtained from the 2D segmentation network is fully integrated into the backbone network. We assess the quality of the proposed method using the Osteoarthritis Initiative (OAI) dataset and the 2010 Grand Challenge Knee Image Segmentation (SKI-10) dataset, comparing it to the Baseline Network and other advanced 2D and 3D segmentation methods. The experiments demonstrate that UVNet achieves competitive performance in the aforementioned two cartilage segmentation tasks.

An IMRT planning technique for treating whole breast or chest wall with regional lymph nodes on Halcyon and Ethos.

Field size limitations on Halcyon and Ethos treatment machines largely preclude use of the conventional monoisocentric three-field technique for breast/chest wall and regional lymph nodes. We present an alternative, IMRT-based planning approach that facilitates treatment on Halcyon and Ethos while preserving plan quality. Eight breast and regional node cases (four left-sided, four right-sided) were planned for an Ethos machine using a 15-17 field IMRT technique. Institutional plan quality metrics for CTV and PTV coverage and OAR sparing were assessed. Five plans (four right-sided, one left-sided) were also planned using a hybrid 3D multisocenter technique. CTV coverage and OAR sparing were compared to the IMRT plans. Eclipse scripting tools were developed to aid in beam placement and plan evaluation through a set of dosimetric scorecards, and both are shared publicly. On average, the IMRT plans achieved breast CTV and PTV coverage at 50Gy of 97.9% and 95.7%, respectively. Supraclavicular CTV and PTV coverages at 45Gy were 100% and 95.5%. Axillary lymph node CTV and PTV coverages at 45Gy were 100% and 97.1%, and IMN CTV coverage at 45Gy was 99.2%. Mean ipsilateral lung V20 Gy was 19.3%, and average mean heart dose was 1.6Gy for right-sided cases and 3.0Gy for left-sided. In comparison to the hybrid 3D plans, IMRT plans achieved higher breast and supraclavicular CTV coverage (99.9%vs. 98.6% and 99.9%vs. 93.4%), higher IMN coverage (99.6%vs. 78.2%), and lower ipsilateral lung V20 Gy (19.6%vs. 28.2%). Institutional plan quality benchmarks were achieved for all eight cases using the IMRT-based planning approach. The IMRT-based planning approach offered superior conformity and OAR sparing than a competing hybrid 3D approach.

A Robotic Arm Visual Grasp Detection Algorithm Combining 2D Images and 3D Point Clouds

Robot grasping detection methods are categorized into two-dimension (2D) and three-dimension (3D) approaches. In 2D grasp detection, gripper pose prediction occurs directly on RedGreenBlue (RGB) images, limiting grasp direction. Conversely, 3D grasp detection predicts gripper pose using 3D point clouds, allowing greater grasp flexibility. However, the data volume of 3D point clouds hampers real-time detection. To address this, this paper proposes a novel grasping detection algorithm that combines 2D images and 3D point clouds. Initially, a Single Shot MultiBox Detector (SSD) network generates 2D prediction boxes on RGB images. Through enhancements to the prior box scaling, the accuracy of bounding the target object is improved. Subsequently, 2D boxes are transformed into 3D frustums, and extraneous data points are removed. By utilizing Random Sampling Consistent Segmentation (RANSAC) and Euclidean Clustering Segmentation Algorithm (ECSA), the target point clouds are isolated, and subsequently, the spatial pose of the target is represented using an Oriented Bounding Box (OBB). Processed point clouds enter an enhanced PointNet Grasp Pose Detection (PointNetGPD) algorithm, In contrast to the original approach involving extensive random sampling of grasp candidates, the enhanced PointNetGPD method enables the selective sampling of grasp candidates by incorporating pose constraints between the target and gripper. Following this, the generated grasp candidates are subjected to evaluation through a scoring process conducted by an assessment network. Ultimately, the robotic arm is guided to perform the grasp associated with the highest score. In the experimental phase, the proposed algorithm demonstrated a high success rate in capturing multiple targets, along with a reduced grasping time. These results underscore the algorithm's superior grasping quality and enhanced real-time performance when compared to similar algorithms.

Impact of Concentration Polarization Phenomena on Gas Separation Processes with High-Performance Zeolite Membranes: Experiments vs. Simulations.

Polarization phenomena play a key role in membrane separation processes but remain largely unexplored for gas separations, where the mass transfer resistance is most often limited to the membrane. This assumption, which is commonly used today for the simulation of membrane gas separations, has to be reconsidered when high-performance materials, showing a very high permeance and/or selectivity, are used. In this study, a series of steady-state separation performances experimentally obtained on CO2/CH4 mixtures with a zeolite membrane are compared to the predictions of a dedicated 1D approach, recently derived and validated through CFD simulations. Polarization effects are shown to generate a significant negative impact on the separation performances, both in terms of the productivity and separation efficiency. The 1D model predictions, based on pure gas permeance data and without any adjustable parameters, are in very good agreement with the experimental data. This fast and efficient modeling approach can easily be implemented in simulation or process synthesis programs for the rigorous evaluation of membrane gas separation processes, when high-performance materials are used.

Construction and characterisation of a structured, tuneable, and transparent 3D culture platform for soil bacteria.

We have developed a tuneable workflow for the study of soil microbes in an imitative 3D soil environment that is compatible with routine and advanced optical imaging, is chemically customisable, and is reliably refractive index matched based on the carbon catabolism of the study organism. We demonstrate our transparent soil pipeline with two representative soil organisms, Bacillus subtilis and Streptomyces coelicolor, and visualise their colonisation behaviours using fluorescence microscopy and mesoscopy. This spatially structured, 3D approach to microbial culture has the potential to further study the behaviour of bacteria in conditions matching their native environment and could be expanded to study microbial interactions, such as competition and warfare.

3D evaluation of the extracellular matrix of hypoxic pancreatic islets using light sheet fluorescence microscopy

ABSTRACT Pancreatic islet transplantation is a promising treatment for type 1 diabetes, but the survival and function of transplanted islets are hindered by the loss of extracellular matrix (ECM) during islet isolation and by low oxygenation upon implantation. This study aimed to evaluate the impact of hypoxia on ECM using a cutting-edge imaging approach based on tissue clearing and 3D microscopy. Human and rat islets were cultured under normoxic (O2 21%) or hypoxic (O2 1%) conditions. Immunofluorescence staining targeting insulin, glucagon, CA9 (a hypoxia marker), ECM proteins (collagen 4, fibronectin, laminin), and E-cadherin (intercellular adhesion protein) was performed on fixed whole islets. The cleared islets were imaged using Light Sheet Fluorescence Microscopy (LSFM) and digitally analyzed. The volumetric analysis of target proteins did not show significant differences in abundance between the experimental groups. However, 3D projections revealed distinct morphological features that differentiated normoxic and hypoxic islets. Under normoxic conditions, ECM could be found throughout the islets. Hypoxic islets exhibited areas of scattered nuclei and central clusters of ECM proteins, indicating central necrosis. E-cadherin was absent in these areas. Our results, demonstrating a diminution of islets’ functional mass in hypoxia, align with the functional decline observed in transplanted islets experiencing low oxygenation after grafting. This study provides a methodology combining tissue clearing, multiplex immunofluorescence, Light Sheet Fluorescence Microscopy, and digital image analysis to investigate pancreatic islet morphology. This 3D approach allowed us to highlight ECM organizational changes during hypoxia from a morphological perspective.

Intra-Oral Flap Splint Fabrication: A 3D Printing and Digital Impression Approach

Intra-Oral Flap Splint Fabrication: A 3D Printing and Digital Impression Approach

Applications of the FEM to pipe whip analysis using coupled modelling technique

Pipelines in Nuclear Power Plants (NPPs) work under high pressure and temperature which may cause a guillotine rupture on the highly pressurized pipe during a sudden fracture due to a failure of any working component. Owing to the serious safety concerns resulting from such failures, it becomes important to understand the behaviour of these pipe structures under dynamic loading, particularly, the pipe whipping effect. Therefore, this research study deals with understanding the structural response of pipe whip with a computationally efficient Finite Element (FE) model.The complete three-dimensional (3D) FE model is modified and coupled with a one-dimensional (1D) FE model (coupled 1D/3D FE model) and solved under the exact transient conditions, and compared with the complete 3D FE model. The computational time of the complete 3D approach was reduced to about 10 times using the coupled 1D/3D model without compromising on the accuracy of the numerical results. Furthermore, the numerical results were found to be in close agreement with the referenced experimental data. This research provides justifiable results from the FE models where the dynamic behaviour of high-pressure pipes under transient conditions can be simulated by using the proposed coupled 1D/3D modelling approach.

Concurrent validity and test-retest reliability for the assessment of body segment lengths using two-dimensional video-recordings in weightlifting

ABSTRACT The aims of this study were 1) to propose a simple two-dimensional (2D) video-based method to measure segment lengths in the clean and jerk during weightlifting competitions, and 2) to check concurrent validity and test-retest reliability compared to 3D video-based measurements. In the first part of the study, key body positions for the assessment of segment lengths via 2D measurement method were determined. In the second part, concurrent validity was verified, comparing the 2D segment length measurements to 3D measurements. In the third part, the test-retest reliability of the 2D approach was investigated. In the clean and in the jerk, the lift-off/start position and the position of maximum barbell velocity displayed a minimised offset between ‘real’ 3D segment lengths and 2D projections of the segment lengths. For the concurrent validity assessment, the overall random measurement error was 4.3% with concordance correlation coefficients ranging from 0.64 to 0.88. The standard error of measurement in the test-retest procedure was 2.4% on average with intraclass correlation coefficients ranging from 0.85 to 0.96. The proposed 2D video-based approach to measure body segment lengths during weightlifting exercises is proven to be a valid and reliable procedure.

Transversely heterogeneous nonlocal Timoshenko beam theory: A reduced-order modeling via distributed-order fractional operators

Nonlocal effects are widely identified in micro/nano systems and have been studied extensively in recent decades. Nevertheless, existing studies mostly focus on the modeling of homogeneous nonlocal systems and as a result, cannot model micro/nanostructures with heterogeneously distributed nonlocal effects. This study introduces a reduced-order nonlocal Timoshenko beam model that facilitates precise modeling of micro-elastic beams with layer-wise heterogeneous nonlocal effects. First, a fully-resolved two-dimensional (2D) plane-strain fractional-order nonlocal elastic framework is developed to provide reference solutions for 2D nonlocal beam problems. Building upon this general nonlocal elasticity theory, an equivalent one-dimensional (1D) nonlocal Timoshenko model is then developed. The layer-wise heterogeneous nonlocal information is captured by using distributed-order (DO) operators, and the impacts arising from the nonlocal heterogeneity are further characterized by introducing two auxiliary parameters. Both 1D and 2D approaches are applied to simulate the mechanical responses of nonlocal beams. Direct comparisons of numerical simulations produced by either the 1D or the fully-resolved 2D model confirm that the DO Timoshenko beam formulation (together with the two auxiliary parameters) can capture not only the overall beam deflection but also the additional shear effect induced by the heterogeneous nonlocality. Computational cost assessment also indicates the proposed approach’s superior performance. The proposed DO Timoshenko model enables model-order reduction without compromising the heterogeneous nonlocal description of the material hence leading to an efficient and accurate reduced-order nonlocal modeling approach that addresses the limitations of prior nonlocal microstructural theories and extends applicability to a broader range of heterogeneous nano/microstructures.

A 3D transfer learning approach for identifying multiple simultaneous errors during radiotherapy

Objective. Deep learning models, such as convolutional neural networks (CNNs), can take full dose comparison images as input and have shown promising results for error identification during treatment. Clinically, complex scenarios should be considered, with the risk of multiple anatomical and/or mechanical errors occurring simultaneously during treatment. The purpose of this study was to evaluate the capability of CNN-based error identification in this more complex scenario. Approach. For 40 lung cancer patients, clinically realistic ranges of combinations of various treatment errors within treatment plans and/or computed tomography (CT) images were simulated. Modified CT images and treatment plans were used to predict 2580 3D dose distributions, which were compared to dose distributions without errors using various gamma analysis criteria and relative dose difference as dose comparison methods. A 3D CNN capable of multilabel classification was trained to identify treatment errors at two classification levels, using dose comparison volumes as input: Level 1 (main error type, e.g. anatomical change, mechanical error) and Level 2 (error subtype, e.g. tumor regression, patient rotation). For training the CNNs, a transfer learning approach was employed. An ensemble model was also evaluated, which consisted of three separate CNNs each taking a region of interest of the dose comparison volume as input. Model performance was evaluated by calculating sample F1-scores for training and validation sets. Main results. The model had high F1-scores for Level 1 classification, but performance for Level 2 was lower, and overfitting became more apparent. Using relative dose difference instead of gamma volumes as input improved performance for Level 2 classification, whereas using an ensemble model additionally reduced overfitting. The models obtained F1-scores of 0.86 and 0.62 on an independent test set for Level 1 and Level 2, respectively. Significance. This study shows that it is possible to identify multiple errors occurring simultaneously in 3D dose verification data.

A Quantitative Method for the Evaluation of Deep Vein Thrombosis in a Murine Model Using Three-Dimensional Ultrasound Imaging.

Deep vein thrombosis (DVT) is a life-threatening condition that can lead to its sequelae pulmonary embolism (PE) or post-thrombotic syndrome (PTS). Murine models of DVT are frequently used in early-stage disease research and to assess potential therapies. This creates the need for the reliable and easy quantification of blood clots. In this paper, we present a novel high-frequency 3D ultrasound approach for the quantitative evaluation of the volume of DVT in an in vitro model and an in vivo murine model. The proposed method involves the use of a high-resolution ultrasound acquisition system and semiautomatic segmentation of the clot. The measured 3D volume of blood clots was validated to be correlated with in vitro blood clot weights with an R2 of 0.89. Additionally, the method was confirmed with an R2 of 0.91 in the in vivo mouse model with a cylindrical volume from macroscopic measurement. We anticipate that the proposed method will be useful in pharmacological or therapeutic studies in murine models of DVT.

Biopathologic Characterization and Grade Assessment of Breast Cancer With 3-D Multiparametric Ultrasound Combining Shear Wave Elastography and Backscatter Tensor Imaging

ObjectiveDespite recent improvements in medical imaging, the final diagnosis and biopathological characterization of breast cancers currently still requires biopsies. Ultrasound is commonly used for clinical examination of breast masses. B-mode and Shear Wave Elastography (SWE) are already widely used to detect suspicious masses and differentiate benign lesions from cancers. But additional ultrasound modalities such as Backscatter Tensor Imaging (BTI) could provide relevant biomarkers related to tissue organization. Here we present a 3D multiparametric ultrasound approach applied to breast carcinomas aiming to (i) validate the ability of BTI to reveal the underlying organization of collagen fibers and (ii) assess the complementarity of SWE and BTI to reveal biopathological features of diagnostic interest. Methods3D SWE and BTI were performed ex vivo on 64 human breast carcinoma samples using a linear ultrasound probe moved by a set of motors. In this paper, we present a 3D multiparametric representation of the breast masses and quantitative measurements combining B-mode, SWE and BTI. ResultsOur results show for the first time that BTI can capture the orientation of the collagen fibers around tumors. BTI was found to be a relevant marker for assessing cancer stages revealing a more tangent tissue orientation for in situ carcinomas than for invasive cancers. In invasive cases, the combination of BTI and SWE parameters allowed for classification of invasive tumors with respect to their grade with an accuracy of 95.7%. ConclusionOur results highlight the potential of 3D multiparametric ultrasound imaging for biopathological characterization of breast tumors.

Unsupervised learning-based approach for detecting 3D edges in depth maps

3D edge features, which represent the boundaries between different objects or surfaces in a 3D scene, are crucial for many computer vision tasks, including object recognition, tracking, and segmentation. They also have numerous real-world applications in the field of robotics, such as vision-guided grasping and manipulation of objects. To extract these features in the noisy real-world depth data, reliable 3D edge detectors are indispensable. However, currently available 3D edge detection methods are either highly parameterized or require ground truth labelling, which makes them challenging to use for practical applications. To this extent, we present a new 3D edge detection approach using unsupervised classification. Our method learns features from depth maps at three different scales using an encoder–decoder network, from which edge-specific features are extracted. These edge features are then clustered using learning to classify each point as an edge or not. The proposed method has two key benefits. First, it eliminates the need for manual fine-tuning of data-specific hyper-parameters and automatically selects threshold values for edge classification. Second, the method does not require any labelled training data, unlike many state-of-the-art methods that require supervised training with extensive hand-labelled datasets. The proposed method is evaluated on five benchmark datasets with single and multi-object scenes, and compared with four state-of-the-art edge detection methods from the literature. Results demonstrate that the proposed method achieves competitive performance, despite not using any labelled data or relying on hand-tuning of key parameters.

High-capacity CO/CO2 methanation reactor design strategy based on 1D PFR modelling and experimental investigation

An in-depth analysis of oil-cooled and naturally ambient air-cooled fixed bed reactors for catalytic methanation of a feedgas containing CO and CO2 has been performed. Combined investigation of modelling and experiments showed, that small tube-to-pellet diameters ratios and optimized reactor cooling are beneficial for high-capacity CO/CO2 methanation. Very good model accuracy was proven with a 1D approach for small diameter reactor pipes. It is shown that the reactor design sweet spot under consideration of input gas capacity, methane output concentration, catalyst degradation and pressure loss can be assessed by the experimentally validated reactor model. The study reveals insights to the mechanism of combined CO and CO2 methanation showing that initial CO methanation is kinetically limited, while subsequent CO2 methanation is ruled by the kinetics of the reverse water gas shift reaction. Finally, this works aim is to provide a design strategy for effective and cheap high-capacity CO/CO2 methanation reactors for industrial scale using commercial pellet catalysts in oil-cooled tube-bundle-reactors.

Geodetic imaging of magma ascent through a bent and twisted dike during the Tajogaite eruption of 2021 (La Palma, Canary Islands)

On Sept. 19th, 2021, the largest historical eruption on the island of La Palma began, which had a significant scientific, social, and economic impact. The 2021 Tajogaite eruption was characterised by short precursors, lasting only 8 days. The seismicity started on Sept. 11th with a westward and upward migration of hypocenters. Permanent GNSS stations started recording deformation on Sept. 12th on the island's western side, which reached more than 15 cm just before the eruption. After the eruption onset, the ground deformation increased, reaching a maximum on Sept. 22nd and showing a nearly steady deflation trend in the following months. To better understand the dynamics of the eruption, we exploited a joint dataset of GNSS and Sentinel-1 SBAS time series along both ascending and descending orbits. To obtain the geometry of the causative source of the ground deformation, we combined the result of a preliminary non-linear inversion and the precise location of the seismicity. The resulting geometry of the source is that of a twisted dike bending eastward. We performed inverse modelling to obtain the spatiotemporal kinematics of the opening function of the dike. The forward modelling has been realised using a 3D finite-element approach considering the island's topography. Our findings reveal a close correspondence between the magmatic intrusion and pre-eruptive seismicity. The ascent of the magma occurred along two branches, and the rheology of a previously identified ductile layer strongly affected the magma propagation process. Finally, we found evidence of an early shallow deformation, which we interpret as the effect of ascending hydrothermal fluids. Our findings highlight the need for advanced modelling to understand pre-eruptive processes in basaltic volcanoes.

Kriterijumi za primenu hibridnog modela 3D katastra u Srbiji

3D cadastre as a modern approach to the development of the cadastral information system brings many advantages and solves the difficulties that the current cadastral system faces when it comes to registration and visualization of complex 3D situations. Accordingly, transitioning to a 3D cadastral system requires significantly more resources because it is necessary to collect, process, store, maintain, and distribute data in three dimensions. To open a more accessible path to implementing a 3D cadastre, a hybrid model is one of the proposed approaches. This implies that current 2D cadastral data are used as much as possible, and that in locations where it is not possible to unambiguously present properties and boundaries, a 3D approach is used. In this paper, the basic criteria defining when a 3D approach is necessary within the hybrid model are discussed and presented. A basic assessment of the relationship between 2D and 3D approaches is provided within a case study.

Analysis of Compound Flooding in the Cakung Drain Area, DKI Jakarta Province

Cakung Drain is an artificial river located downstream of the Cakung and Buaran Rivers, established as part of Jakarta's flood control infrastructure. Administratively, the upper section of Cakung Drain is located in East Jakarta, while the lower section is in North Jakarta. The current capacity of Cakung Drain is insufficient to handle the flow during flood conditions, leading to persistent flooding in the area. Flood conditions downstream of Cakung Drain are exacerbated by the potential for Compound Flood, resulting from the simultaneous occurrence of two extreme conditions. Various initiatives to manage floods have been implemented in the Cakung Drain area. This study aims to evaluate the flood risk in the Cakung Drain area before and after the implementation of flood management structures, considering the impact of discharge and tidal conditions downstream influenced by waves and storm surges. Flood modelling is executed using HECRAS software employing both 1D and 2D approaches. The study's findings reveal that the flooded area under existing conditions spans 7.065 km2. Under design conditions, flooding persists with an area of 4.15 km², attributed to flood overflow in downstream areas where embankments have not been constructed.

Developing a multimodal imaging pipeline for molecular biochemical studies with a 3D approach

Developing a multimodal imaging pipeline for molecular biochemical studies with a 3D approach

Segmentation of Head and Neck Tumors Using Dual PET/CT Imaging: Comparative Analysis of 2D, 2.5D, and 3D Approaches Using UNet Transformer

Segmentation of Head and Neck Tumors Using Dual PET/CT Imaging: Comparative Analysis of 2D, 2.5D, and 3D Approaches Using UNet Transformer