2-dimensional Projection Research Articles

3DVascNet: An Automated Software for Segmentation and Quantification of Mouse Vascular Networks in 3D.

Analysis of vascular networks is an essential step to unravel the mechanisms regulating the physiological and pathological organization of blood vessels. So far, most of the analyses are performed using 2-dimensional projections of 3-dimensional (3D) networks, a strategy that has several obvious shortcomings. For instance, it does not capture the true geometry of the vasculature and generates artifacts on vessel connectivity. These limitations are accepted in the field because manual analysis of 3D vascular networks is a laborious and complex process that is often prohibitive for large volumes. To overcome these issues, we developed 3DVascNet, a deep learning-based software for automated segmentation and quantification of 3D retinal vascular networks. 3DVascNet performs segmentation based on a deep learning model, and it quantifies vascular morphometric parameters such as vessel density, branch length, vessel radius, and branching point density. We tested the performance of 3DVascNet using a large data set of 3D microscopy images of mouse retinal blood vessels. We demonstrated that 3DVascNet efficiently segments vascular networks in 3D and that vascular morphometric parameters capture phenotypes detected by using manual segmentation and quantification in 2 dimension. In addition, we showed that, despite being trained on retinal images, 3DVascNet has high generalization capability and successfully segments images originating from other data sets and organs. Overall, we present 3DVascNet, a freely available software that includes a user-friendly graphical interface for researchers with no programming experience, which will greatly facilitate the ability to study vascular networks in 3D in health and disease. Moreover, the source code of 3DVascNet is publicly available, thus it can be easily extended for the analysis of other 3D vascular networks by other users.

Diagnostic Method for Hydropower Plant Condition-based Maintenance combining Autoencoder with Clustering Algorithms

The French company EDF uses supervisory control and data acquisition systems in conjunction with a data management platform to monitor hydropower plant, allowing engineers and technicians to analyse the time-series collected. Depending on the strategic importance of the monitored hydropower plant, the number of time-series collected can vary greatly making it difficult to generate valuable information from the extracted data. In an attempt to provide an answer to this particular problem, a condition detection and diagnosis method combining clustering algorithms and autoencoder neural networks for pattern recognition has been developed and is presented in this paper.First, a dimension reduction algorithm is used to create a 2- or 3-dimensional projection that allows the users to identify unsuspected relationships between datapoints.Then, a collection of clustering algorithms regroups the datapoints into clusters. For each identified cluster, an autoencoder neural network is trained on the corresponding dataset. The aim is to measure the reconstruction error between each autoencoder model and the measured values, thus creating a proximity index for each state discovered during the clustering stage.

Intraoperative fluoroscopic safety assessment of femoral head implants with 3-dimensional risk parameters to minimize cut-out.

This study aimed to introduce a method to extract the 3-dimensional spatial position of the femoral head implant from 2-dimensional fluoroscopic projections, allowing surgeons to assess fixation much more accurately and prevent cut-out complications in proximal femoral nailing. To define a safety region for the tip in the femoral head, a novel 3-dimensional distance-based risk parameter called TSD3D was introduced. An intersection algorithm was developed that solely takes the fluoroscopic anteroposterior and lateral distances to reveal the 3-dimensional location of the screw or Kirschner wire tip, enabling the utilization of the 3-dimensional parameter. Orthogonal per- spectives of 6 femur proximal bone substitutes with randomly inserted Kirschner wires were imaged under fluoroscopy. The developed algorithm was used to calculate the implant tip location in 3-dimensional from 2-dimensional images for each case. Algorithm accuracy was validated with the computed tomography-obtained 3-dimensional models of the same femur substitutes. The newly introduced risk parameter successfully visualizes 3-dimensional safety regions. Utilizing the 2-dimensional fluoro- scopic distances as inputs to the algorithm, the 3-dimensional position of the implanted Kirschner wire tip is calculated with a maximum of 9.8% error for a single Cartesian-coordinate measurement comparison. By incorporating the newly introduced 3-dimensional risk parameter, surgeons can more precisely evaluate the position of the implant and avoid cut-out complications, instead of relying solely on misleading 2-dimensional fluoroscopic projections of the femoral head.

An unsettling re-composition: Istanbul's lost Armenian April 11 Memorial

This research uses 3D drawing techniques to create a re-composition of the lost April 11 Memorial, a contested monument which commemorated the deportation of Armenian intellectuals from Istanbul in 1915. We use the only photograph of the monument remaining in the public record as a witness to inform this re-composition and multiply the possibilities of remembering through the medium of drawing. The memorial's absence in the archive can be viewed as an extension of the violence Armenians endured during the deportations and subsequent massacres of the twentieth century, as well as a prolonged suppression of the Armenian people's right to memory within Turkey's unequal historical geography. For this reason, we represent the monument in a new way, in a new medium. Our research explores the value of non-physical reproductions of lost monuments. The re-composition takes place through making a visual and spatial analysis of the photograph, and reveals how drawing techniques, that use 3-dimensional projections, can be used to overcome the absence of knowledge. This method allows us to speculate on the lost monument, to reappropriate and carry it to the present by producing a new visual archive. This act also calls for the profanation, in Agamben's sense, of the memorial by unsettling and removing this forbidden object from the realm of the sacred and the inviolable. Through play and design, we open up a new use and space for the lost memorial.

Runge–Lenz vector as a 3d projection of SO(4) moment map in phase space

We show, using the methods of geometric algebra, that Runge–Lenz vector in the Kepler problem is a 3-dimensional projection of SO(4) moment map that acts on the phase space of 4-dimensional particle motion. Thus, Runge–Lenz vector is a consequence of geometric symmetry of phase space.

Vortex Veins in Eyes With Pachychoroid Spectrum Disorders Evaluated by the Adjusted Reverse 3-Dimensional Projection Model

To compare the distribution of vortex vein ampulla (VVA) between pachychoroid spectrum disorder (PSD) and controls. A single-center, case-control study. This study included 75 PSD, 35 fellow, and 65 control eyes. We quantified VVA distribution using a 3-dimensional reverse projection model corrected for image distortion. We investigated the distribution of major drainage veins (MDV), in which macular Haller's vessels directly influx. The mean distances from the optic disc to VVAs and the mean angles between VVAs and the fovea-disc line. The PSD group had significantly fewer VVA in infranasal sector (PSD, fellow, control; 1.6±0.6, 1.8±0.6, 1.9±0.6, respectively, P= 0.026). In supralateral sector, for PSD, fellows, and controls, the mean distances from the optic disc to VVAs were 14.1±1.0 mm, 14.1±1.1 mm, and 13.6±1.4 mm, respectively, and were significantly farther in PSD than in controls (P= 0.023). The mean angles between VVAs and the fovea-disc line were 64.8±5.9°, 66.4±6.4°, and 61.7±6.4°, respectively, and were significantly higher in PSD and fellows than in controls (P=0.008). The mean distances from the optic disc to MDV in supratemporal sector were 14.1±1.2 and 13.7±1.2 in eyes whose Haller's vessels extended beyond the fovea-disc line (asymmetry), and those that did not, respectively, with the asymmetric eyes significantly farther (P= 0.016). The VVA position in supralateral sector was farther and higher in PSD than in controls, suggesting that the distribution of VVA may be associated with the development of PSD. Proprietary or commercial disclosure may be found after the references.

Batch size: go big or go home? Counterintuitive improvement in medical autoencoders with smaller batch size.

Batch size is a key hyperparameter in training deep learning models. Conventional wisdom suggests larger batches produce improved model performance. Here we present evidence to the contrary, particularly when using autoencoders to derive meaningful latent spaces from data with spatially global similarities and local differences, such as electronic health records (EHR) and medical imaging. We investigate batch size effects in both EHR data from the Baltimore Longitudinal Study of Aging and medical imaging data from the multimodal brain tumor segmentation (BraTS) challenge. We train fully connected and convolutional autoencoders to compress the EHR and imaging input spaces, respectively, into 32-dimensional latent spaces via reconstruction losses for various batch sizes between 1 and 100. Under the same hyperparameter configurations, smaller batches improve loss performance for both datasets. Additionally, latent spaces derived by autoencoders with smaller batches capture more biologically meaningful information. Qualitatively, we visualize 2-dimensional projections of the latent spaces and find that with smaller batches the EHR network better separates the sex of the individuals, and the imaging network better captures the right-left laterality of tumors. Quantitatively, the analogous sex classification and laterality regressions using the latent spaces demonstrate statistically significant improvements in performance at smaller batch sizes. Finally, we find improved individual variation locally in visualizations of representative data reconstructions at lower batch sizes. Taken together, these results suggest that smaller batch sizes should be considered when designing autoencoders to extract meaningful latent spaces among EHR and medical imaging data driven by global similarities and local variation.

An event display for the Transition Radiation Detector in ALICE

Particle collider experiments generate huge volumes of complex data, and event displays provide a useful visual representation to accelerate the learning process towards physics results. Event displays are also used to verify expected behaviour, identify anomalous data, or explain important results. They often have a steep learning curve, a high barrier to entry, or are tightly bound to a specific environment. The upgrade of ALICE in preparation for Run 3 of the LHC requires modifications to existing event displays. We present here a cross-platform, browser-based event display, focused on interactive 2-dimensional projections of collision data from ALICE, specifically focused on the operation of the Transition Radiation Detector (TRD). It is driven by a flexible intermediate JSON data format suitable for web-based displays, and a generic task to convert existing data acquired in previous runs to this format. The relationship between raw and reconstructed data in the TRD is illustrated through a novel pairing of raw and reconstructed data in a unified interactive view. A formal design study methodology was used to guide these choices, and the display was evaluated by both scientists and the public, through a series of case studies.

Dynamical analysis and boundedness for a generalized chaotic Lorenz model

<abstract><p>The dynamical behavior of a 5-dimensional Lorenz model (5DLM) is investigated. Bifurcation diagrams address the chaotic and periodic behaviors associated with the bifurcation parameter. The Hamilton energy and its dependence on the stability of the dynamical system are presented. The global exponential attractive set (GEAS) is estimated in different 3-dimensional projection planes. A more conservative bound for the system is determined, that can be applied in synchronization and chaos control of dynamical systems. Finally, the finite time synchronization of the 5DLM, indicating the role of the ultimate bound for each variable, is studied. Simulations illustrate the effectiveness of the achieved theoretical results.</p></abstract>

Generative models for age, race/ethnicity, and disease state dependence of physiological determinants of drug dosing.

Dosing requires consideration of diverse patient-specific factors affecting drug pharmacokinetics and pharmacodynamics. The available pharmacometric methods have limited capacity for modeling the inter-relationships and patterns of variability among physiological determinants of drug dosing (PDODD). To investigate whether generative adversarial networks (GANs) can learn a generative model from real-world data that recapitulates PDODD distributions. A GAN architecture was developed for modeling a PDODD panel comprised of: age, sex, race/ethnicity, body weight, body surface area, total body fat, lean body weight, albumin concentration, glomerular filtration rate (EGFR), urine flow rate, urinary albumin-to-creatinine ratio, alanine aminotransferase to alkaline phosphatase R-value, total bilirubin, active hepatitis B infection status, active hepatitis C infection status, red blood cell, white blood cell, and platelet counts. The panel variables were derived from National Health and Nutrition Examination Survey (NHANES) data sets. The dependence of GAN-generated PDODD on age, race, and active hepatitis infections was assessed. The continuous PDODD biomarkers had diverse non-normal univariate distributions and bivariate trend patterns. The univariate distributions of PDODD biomarkers from GAN simulations satisfactorily approximated those in test data. The joint distribution of the continuous variables was visualized using three 2-dimensional projection methods; for all three methods, the points from the GAN simulation random variate vectors were well dispersed amongst the test data. The age dependence trend patterns in GAN data were similar to those in test data. The histograms for R-values and EGFR from GAN simulations overlapped extensively with test data histograms for the Hispanic, White, African American, and Other race/ethnicity groups. The GAN-simulated data also mirrored the R-values and EGFR changes in active hepatitis C and hepatitis B infection. GANs are a promising approach for simulating the age, race/ethnicity and disease state dependencies of PDODD.

Predicting beam transmission using 2-dimensional phase space projections of hadron accelerators

We present a method to compress the 2D transverse phase space projections from a hadron accelerator and use that information to predict the beam transmission. This method assumes that obtaining at least three projections of the 4D transverse phase space is possible and that an accurate simulation model is available for the beamline. Using a simulated model, we show that—a computer can train a convolutional autoencoder to reduce phase-space information which can later be used to predict the beam transmission. Finally, we argue that although using projections from a realistic nonlinear distribution produces less accurate results, the method still generalizes well.

Reliability and criterion validity of two-dimensional movement assessments in those with patellofemoral pain

ObjectivesDetermine criterion validity and intra/inter-rater reliability of 2-dimensional (2D) knee frontal plane projection angle (kFPPA), hip frontal plane projection angle (hFPPA), and dynamic valgus index (DVI) during forward step-downs in those with patellofemoral pain (PFP). DesignCross-sectional. SettingUniversity research laboratory. Participants39 participants with PFP (34.18 ± 7.41years, 170± .1 cm, 81.03 ± 19.36 kg, duration of pain: 68.67 ± 85.08months, anterior knee pain scale: 80.49 ± 7.87, visual analog scale:2.08 ± 2.02) Main outcome measuresAverage 3D hip and knee sagittal, frontal, and transverse joint angles and 2D kFPPA, hFPPA, and DVI at maximum knee flexion were variables of interest. 3D DVI was calculated as the sum of hip and knee frontal and transverse angles. 2D kFPPA, hFPPA, and DVI were calculated by two raters independently on two occasions. ResultsIntra- and inter-rater reliability of all 2D angles were excellent. kFPPA was moderately correlated to 3D knee transverse angles. hFPPA was moderately correlated to 3D hip frontal and transverse angles and largely correlated to 3D DVI. 2D DVI was moderately correlated to hip transverse angles. ConclusionkFPPA, hFPPA, and DVI are reliable. hFPPA may be reflective of 3D hip and knee frontal and transverse motion during forward step-downs in those with PFP.

Cascaded Sobol' sampling

Rendering quality is largely influenced by the samplers used in Monte Carlo integration. Important factors include sample uniformity (e.g., low discrepancy) in the high-dimensional integration domain, sample uniformity in lower-dimensional projections, and lack of dominant structures that could result in aliasing artifacts. A widely used and successful construction is the Sobol' sequence that guarantees good high-dimensional uniformity and consequently results in faster convergence of quasi-Monte Carlo integration. We show that this sequence exhibits low uniformity and dominant structures in low-dimensional projections. These structures impair quality in the context of rendering, as they precisely occur in the 2-dimensional projections used for sampling light sources, reflectance functions, or the camera lens or sensor. We propose a new cascaded construction, which, despite dropping the sequential aspect of Sobol' samples, produces point sets exhibiting provably perfect dyadic partitioning (and therefore, excellent uniformity) in consecutive 2-dimensional projections, while preserving good high-dimensional uniformity. By optimizing the initialization parameters and performing Owen scrambling at finer levels of binary representations, we further improve over Sobol's integration convergence rate. Our method does not incur any overhead as compared to the generation of the Sobol' sequence, is compatible with Owen scrambling and can be used in rendering applications.

Reduction of Drag Resistance by Pressure Drop in Pipeline with 3-Dimensional Design Optimization

The searching for the most optimal pipeline route is a crucial problem in the maritime world because it consumes total designing time by 50%. Also, with different types of ships increases the design complexity. The usual design process has not considered the aspect of distance, cost, obstacles, drag, and pressure reduction in the pipeline very accurately. However, along with algorithms' development to optimize pipeline design, the time can be cut by 40%. This research uses computer-generated Dijkstra's algorithm to optimize pipeline design by considering several constraints in pipe spacing, the number of bends, crossings, pipeline stacks to improve drag reduction, and reducing pressure. This research was conducted to see the effect of pipe mapping on pressure drop, which is too influenced by human decisions that cannot consider bending, crossing, pipe piling, and bending of pipes that are too many to be considered by humans. Helping humans choose pipe mappings with various considerations that can affect pressure drop is advantageous because mapping helps to cut production times and produce a more efficient flow. In this study, this research aims to produce pressure drop by mapping pipes using the Djikstra algorithm by considering bending, crossing, and stacking, which are presented based on the 2-dimensional and 3-dimensional mapping. The data generated in the way of a comparison between drag reduction and pressure drop in pipe design optimization utilizing Dijkstra's and without using the Dijkstra's algorithm with 3-dimensional projections. The result shows the improvement of the drag reduction rate by 8% by decreasing pressure drop by 13%.

Quantitative Parameters from OCT Angiography in Patients with Diabetic Retinopathy and in Those with Only Peripheral Retinopathy Compared with Control Participants.

PurposeTo describe the differences in a range of quantitative OCT angiography (OCTA) metrics across early stages of diabetic retinopathy (DR), providing robust effect estimates as well as sensitivity and specificity.DesignCross-sectional study with population-based sampling.ParticipantsFour hundred forty-one eyes from 296 individuals: 328 control eyes (no diabetes mellitus [DM] and no DR), 55 eyes with DM and no DR, and 58 eyes with early nonproliferative DR.MethodsMultimodal retinal imaging included color fundus photography, color Optomap ultra-widefield imaging, and spectral-domain OCT (Spectralis OCT2; Heidelberg Engineering GmbH) with the OCTA module. All images were graded for the presence and severity of DR features. OCTA images were assessed manually for inclusion based on quality. Binary OCTA metrics were assessed after 3-dimensional projection artifact removal including from the nerve fiber layer vascular plexus, superficial vascular plexus (SVC), and deep vascular plexus (DVC) by Early Treatment Diabetic Retinopathy Study (ETDRS) grid, foveal avascular zone (FAZ) area, FAZ minimum and maximum diameter, perimeter length, and circularity.Main Outcome MeasuresDiabetes mellitus and DR status and presence or absence of DR in the retinal periphery.ResultsThe reduction in vessel densities in participants with DM and manifest DR compared with control participants tended to be twice that of those with DM, but no DR, compared with control participants. Some evidence of spatial heterogeneity in vessel reductions was found in those yet to develop DR, whereas those with manifest DR had significant reductions across the ETDRS grid. The FAZ perimeter and circularity were impacted most significantly by DM, and those with DR showed decreased multispectral fractal dimensions compared with control participants. Eyes with peripheral DR had reduced vessel density compared with those with DM and no DR only in the superior outer, temporal inner, and temporal outer regions in the DVC and SVC. The area under the receiver operating characteristic curve ranged between 0.48 and 0.73.ConclusionsSignificant differences in OCTA metrics can be found in those with DM before manifest DR using commercially available equipment with minimal image postprocessing. Although diagnostic performance was poor, these metrics may be useful for measuring change over time in clinical trials.

A Probabilistic Proof of the Spherical Excess Formula

A probabilistic proof of Girard’s angle excess formula for the area of a spherical triangle emerges from the observation that an unbounded 3-dimensional convex cone, with single vertex at the origin, has only three kinds of 2-dimensional orthogonal projections: a 2-dimensional convex cone with one vertex, a 2-dimensional half-plane (an outcome with probability zero), and a 2-dimensional plane.

Multiple maneuvering extended targets detection by 3D projection and tracklet association

In this paper, the five fundamental issues of target extension, target maneuvering, closely spaced targets, weak targets, and dense clutter are tackled simultaneously in the framework of a new tracking approach that is capable of effectively detecting multiple closely-spaced maneuvering weak and extended targets in densely cluttered environment. The proposed technique finds its foundation in the track-before-detect (TBD) paradigm, proven to be effective in tracking weak targets in presence of dense clutter. The approach consists of two stages. In the first stage, a 3-dimensional projection based TBD algorithm allows addressing the problem of target extension, weak targets, and dense clutter. Straight line tracklets are obtained at the end of the first stage. In the second stage, tracklets association is performed to obtain the target trajectories. The issue of closely spaced targets and target maneuvering is addressed in this second stage. To illustrate the effectiveness of the proposed approach, synthetic data, real data from a netted radar system, and real data from a high resolution radar are processed both with the proposed algorithm and with several existing algorithms. The obtained results reveal that the proposed method can outperform the competing approaches in various scenarios.

Intraoperative 3-dimensional Projection of Blood Vessels on Body Surface Using an Augmented Reality System.

Summary: Preoperative understanding of the running pattern of blood vessels is an important factor to approach surgical fields safely. In 2 cases where the vascular abnormalities were estimated, we projected the blood vessels onto the surgical field using an augmented reality device HoloLens. A splint was made to allow the patient to be fixed while undergoing computed tomographic angiography. Three-dimensional (3D) data on the blood vessels, skin surfaces, bones, and the 3 chosen points for alignment were segmented and then projected onto the body surfaces as holograms using the HoloLens. Two types of projection for holograms were used: projection type 1—where the body contours were projected as a line, and projection type 2—where the body surface was projected as meshed skin type. By projecting projection type 2 rather than projection type 1, we gained a better understanding of the 3D anatomic findings and deformation characteristics, including the anatomic blood vessel variation and positional relationships between the organs and body surfaces. To some extent, we could make sure that the depth perception can be obtained by recognizing the bone, vessels, or tumor inside the meshed skin surface. Our new method allows the 3D visualization of blood vessels from the body surface, and helps understand the 3D anatomic variation of the blood vessels to be applied as long as the blood vessels can be visualized.

Investigating biases in the measurement of apparent alveolar septal wall thickness with hyperpolarized 129Xe MRI.

To investigate biases in the measurement of apparent alveolar septal wall thickness (SWT) with hyperpolarized xenon-129 (HXe) as a function of acquisition parameters. The HXe MRI scans with simultaneous gas-phase and dissolved-phase excitation were performed using 1-dimensional projection scans in mechanically ventilated rabbits. The dissolved-phase magnetization was periodically saturated, and the dissolved-phase xenon uptake dynamics were measured at end inspiration and end expiration with temporal resolutions up to 10 ms using a Look-Locker-type acquisition. The apparent alveolar septal wall thickness was extracted by fitting the signal to a theoretical model, and the findings were compared with those from the more commonly use chemical shift saturation recovery MRI spectroscopy technique with several different delay time arrangements. It was found that repeated application of RF saturation pulses in chemical shift saturation recovery acquisitions caused exchange-dependent gas-phase saturation that heavily biased the derived SWT value. When this bias was reduced by our proposed method, the SWT dependence on lung inflation disappeared due to an inherent insensitivity of HXe dissolved-phase MRI to thin alveolar structures with very short . Furthermore, perfusion-based macroscopic gas transport processes were demonstrated to cause increasing apparent SWTs with TE (2.5 μm/ms at end expiration) and a lung periphery-to-center SWT gradient. The apparent SWT measured with HXe MRI was found to be heavily dependent on the acquisition parameters. A method is proposed that can minimize this measurement bias, add limited spatial resolution, and reduce measurement time to a degree that free-breathing studies are feasible.

Complete visualization of epidermal margin during ex vivo confocal microscopy of excised tissue with 3-dimensional mosaicking and intensity projection

Ex vivo confocal microscopy allows evaluation of margins of freshly excised tissue for residual basal cell carcinoma (BCC) during staged excision. This approach helps assess margins in the initial stages of excision, while histopathology provides final-stage confirmation for clearance. 1 Jain M. Rajadhyaksha M. Nehal K. Implementation of fluorescence confocal mosaicking microscopy by “early adopter” Mohs surgeons and dermatologists: recent progress. J Biomed Opt. 2017; 22: 24002-24078 Crossref PubMed Scopus (23) Google Scholar However, with the existing tissue mounting devices 1 Jain M. Rajadhyaksha M. Nehal K. Implementation of fluorescence confocal mosaicking microscopy by “early adopter” Mohs surgeons and dermatologists: recent progress. J Biomed Opt. 2017; 22: 24002-24078 Crossref PubMed Scopus (23) Google Scholar ,2 Pérez-Anker J. Puig S. Malvehy J. A fast and effective option for tissue flattening: optimizing time and efficacy in ex vivo confocal microscopy. J Am Acad Dermatol. 2020; 82: e157-e158 Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar flattening the peripheral edges of excised tissue on the cover glass window of the microscope, and imaging of the complete epidermis, which is crucial for the detection of underlying superficial BCCs (sBCC), remains challenging.