Three-dimensional Data Sets Research Articles

Comapping Cellular Content and Extracellular Matrix with Hemodynamics in Intact Arterial Tissues Using Scanning Immunofluorescent Multiphoton Microscopy.

Deviation of blood flow from an optimal range is known to be associated with the initiation and progression of vascular pathologies. Important open questions remain about how the abnormal flow drives specific wall changes in pathologies such as cerebral aneurysms where the flow is highly heterogeneous and complex. This knowledge gap precludes the clinical use of readily available flow data to predict outcomes and improve treatment of these diseases. As both flow and the pathological wall changes are spatially heterogeneous, a crucial requirement for progress in this area is a methodology for acquiring and comapping local vascular wall biology data with local hemodynamic data. Here, we developed an imaging pipeline to address this pressing need. A protocol that employs scanning multiphoton microscopy was developed to obtain three-dimensional (3D) datasets for smooth muscle actin, collagen, and elastin in intact vascular specimens. A cluster analysis was introduced to objectively categorize the smooth muscle cells (SMC) across the vascular specimen based on SMC actin density. Finally, direct quantitative comparison of local flow and wall biology in 3D intact specimens was achieved by comapping both heterogeneous SMC data and wall thickness to patient-specific hemodynamic results.

Improving the Quality of Spontaneously Growing HviGH11 Crystals by Increasing the Viscosity Using Polyethylene Glycols

Proteins can form crystals spontaneously without crystallization experiments. These crystals can be used to determine three-dimensional structures. However, when X-ray diffraction is poor, crystal optimization is required to obtain a high-resolution crystal structure. Endo-1,4-β-xylanase from the fungus Hypocrea virens (HviGH11) spontaneously formed microcrystals after affinity purification and concentration; however, most HviGH11 microcrystals showed poor diffraction in the synchrotron X-ray and X-ray free-electron laser, so a complete three-dimensional structure could not be obtained. This study presents a method to improve the crystal quality of spontaneously grown HviGH11 microcrystals. The crystallization screening results revealed that temperature, pH, and salt were not crucial factors in increasing the solubility or preventing the spontaneous crystal growth of HviGH11. Conversely, the addition of polyethylene glycols (PEGs) as a precipitant facilitated the growth of larger HviGH11 crystals. The improved large HviGH11 crystal showed a diffraction of up to 1.95 Å when exposed to synchrotron X-rays, providing a complete three-dimensional structural dataset. Based on the nucleation rate equation, it was suggested that PEG increases the viscosity of the protein solution rather than promoting nucleation. This increase in viscosity reduced nucleation and facilitated the growth of larger HviGH11 crystals. These results provide valuable insights for future experiments aimed at increasing the size of spontaneously grown crystals.

'Choroid bar': easy-to-seek marker of normal posterior fossa at 12-14 weeks' gestation.

Our objectives were: (1) to assess the visualization rate of the choroid bar in a consecutive series of 306 first-trimester scans; (2) to verify, in this cohort of fetuses, the normality of the posterior fossa later in pregnancy; and (3) to confirm the non-visualization of the choroid bar in a retrospective series of fetuses with posterior fossa malformations. This study included a prospective and a retrospective series. The former comprised 306 fetuses undergoing routine obstetric ultrasound at our unit in both the first and second trimesters over a 6-month period, while the latter comprised 12 cases of posterior fossa malformations. In the prospective study, the presence of the choroid bar, which is defined as a visually continuous, homogeneously hyperechogenic, thick structure bridging the cisterna magna from side to side, was evaluated at the end of the first-trimester nuchal translucency scan. In the retrospective study, previously acquired three-dimensional volume datasets were processed in order to assess whether the choroid bar could be visualized in cases of open spinal dysraphisms and vermian cystic anomalies. In the prospective study, confirmation of a normal posterior fossa was based on the sonographic features of this anatomical region at the time of the second-trimester anomaly scan at 19-21 weeks' gestation, while, in the retrospective study, it was based on autopsy results, when available, or further direct imaging of the defect later in pregnancy. In the prospective study, the choroid bar could be visualized in all 306 fetuses, on transabdominal ultrasound in 287 (93.8%) cases and on transvaginal ultrasound in 19 (6.2%) cases. The choroid bar was displayed with a ventral/dorsal approach in 67 (21.9%) cases, with a lateral approach in 56 (18.3%) cases and with both in 183 (59.8%) cases. All 306 cases were confirmed to have a sonographically normal posterior fossa at 19-21 weeks. On the other hand, in the retrospective study, it was not possible to visualize the choroid bar in any of the fetuses with posterior fossa malformations. We have described a new sign, the choroid bar, consistent with a normal posterior fossa at 12-14 weeks' gestation. The choroid bar provides the option of screening for major abnormalities of the posterior fossa, since its absence raises suspicion of both open spinal dysraphisms and posterior fossa cystic malformations. At the same time, it is easy to visualize, as it can be seen with all lines of insonation. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Direct three-dimensional segmentation of prostate glands with nnU-Net.

In recent years, we and others have developed non-destructive methods to obtain three-dimensional (3D) pathology datasets of clinical biopsies and surgical specimens. For prostate cancer risk stratification (prognostication), standard-of-care Gleason grading is based on examining the morphology of prostate glands in thin 2D sections. This motivates us to perform 3D segmentation of prostate glands in our 3D pathology datasets for the purposes of computational analysis of 3D glandular features that could offer improved prognostic performance. To facilitate prostate cancer risk assessment, we developed a computationally efficient and accurate deep learning model for 3D gland segmentation based on open-top light-sheet microscopy datasets of human prostate biopsies stained with a fluorescent analog of hematoxylin and eosin (H&E). For 3D gland segmentation based on our H&E-analog 3D pathology datasets, we previously developed a hybrid deep learning and computer vision-based pipeline, called image translation-assisted segmentation in 3D (ITAS3D), which required a complex two-stage procedure and tedious manual optimization of parameters. To simplify this procedure, we use the 3D gland-segmentation masks previously generated by ITAS3D as training datasets for a direct end-to-end deep learning-based segmentation model, nnU-Net. The inputs to this model are 3D pathology datasets of prostate biopsies rapidly stained with an inexpensive fluorescent analog of H&E and the outputs are 3D semantic segmentation masks of the gland epithelium, gland lumen, and surrounding stromal compartments within the tissue. nnU-Net demonstrates remarkable accuracy in 3D gland segmentations even with limited training data. Moreover, compared with the previous ITAS3D pipeline, nnU-Net operation is simpler and faster, and it can maintain good accuracy even with lower-resolution inputs. Our trained DL-based 3D segmentation model will facilitate future studies to demonstrate the value of computational 3D pathology for guiding critical treatment decisions for patients with prostate cancer.

A Three-Dimensional Spatial Interpolation Method and Its Application to the Analysis of Oxygen Deficit in the Bohai Sea in Summer

Dissolved oxygen (DO) plays a pivotal role in sustaining marine ecosystems. The Bohai Sea in China is a semi-enclosed sea, and oxygen-deficit events occur from time to time due to human activities. At present, there is a notable absence of any convenient and precise method for obtaining three-dimensional spatial data on DO, and the exploration of the physical mechanisms influencing oxygen deficit remains incomplete. This investigation uses the linear radial basis function (RBF-Linear) fitting method to conduct three-dimensional spatial interpolation for DO, which demonstrates minimal inaccuracy. Then, the RBF-Linear fitting method is utilized to collect a comprehensive three-dimensional spatial dataset encompassing temperature, salinity, and DO in the Bohai Sea in August from 2016 to 2018. The results indicate discernible interannual variations in the extent, area, and distribution of oxygen deficiency during summer in the Bohai Sea. Mechanism analysis reveals that intense precipitation episodes and an increase in wind stress curl exacerbate oxygen depletion. Additionally, the degree, location, and area of the two oxygen-deficit cores (off the Yellow River Estuary and off the Qinhuangdao) in the Bohai Sea are influenced by several factors, including current velocity, direction, local circulation position, and net horizontal transport rate. Furthermore, the study suggests that oxygen deficiency in the Bohai Sea region is currently in its early stages, with a limited degree of injury and a restricted range of influence. The use of a three-dimensional spatial interpolation method to create a complete DO field in three-dimensional space simplifies the research challenges associated with marine oxygen deficit. Moreover, this study holds particular significance for guiding the development of marine fisheries.

Motion Artifact Reduction Using U-Net Model with Three-Dimensional Simulation-Based Datasets for Brain Magnetic Resonance Images.

This study aimed to remove motion artifacts from brain magnetic resonance (MR) images using a U-Net model. In addition, a simulation method was proposed to increase the size of the dataset required to train the U-Net model while avoiding the overfitting problem. The volume data were rotated and translated with random intensity and frequency, in three dimensions, and were iterated as the number of slices in the volume data. Then, for every slice, a portion of the motion-free k-space data was replaced with motion k-space data, respectively. In addition, based on the transposed k-space data, we acquired MR images with motion artifacts and residual maps and constructed datasets. For a quantitative evaluation, the root mean square error (RMSE), peak signal-to-noise ratio (PSNR), coefficient of correlation (CC), and universal image quality index (UQI) were measured. The U-Net models for motion artifact reduction with the residual map-based dataset showed the best performance across all evaluation factors. In particular, the RMSE, PSNR, CC, and UQI improved by approximately 5.35×, 1.51×, 1.12×, and 1.01×, respectively, and the U-Net model with the residual map-based dataset was compared with the direct images. In conclusion, our simulation-based dataset demonstrates that U-Net models can be effectively trained for motion artifact reduction.

Numerical biomechanical finite element analysis of different trimming line designs of orthodontic aligners: An in silico study

A finite element model was used to investigate the effect of different designs and thicknesses of orthodontic aligner margins on their biomechanical behavior. A three-dimensional data set of an upper jaw was imported into the 3-matic software. The upper right central incisor tooth (Tooth 11) was separated from the remaining model, and its periodontal ligament and surrounding bone were designed. Aligners were designed with four different trimming lines (scalloped, straight, scalloped extended, straight extended), each with four different thicknesses (0.3, 0.4, 0.5, and 0.6 mm). The models were imported into a finite element package (Marc/Mentat). A linear elastic constitutive material model was applied. A facial 0.2 mm bodily malalignment of tooth 11 was simulated. The maximum resultant force was in the range of 1.0 N to 2.2 N. The straight trimming designs deliver higher resultant forces compared with scalloped trimming designs. Increasing the aligner thickness and/or extending the aligner edge beyond the gingival line leads to an increase in the resultant force. All designs showed an uneven distribution of the normal contact forces over the tooth surface with a predominant concentration toward the cervical third and distal third, particularly with the extended trimming designs. All designs showed uncontrolled tipping of the tooth. Based on the current model outcomes, the use of a straight extended trimming line design for aligners is favored because of its positive impact on force distribution and, consequently, the control of tooth movement. These findings provide aligner companies and orthodontists a valuable biomechanical evidence and guidance to enhance control over tooth movement and therefore optimize treatment outcomes. This can be achieved by trimming the edges of aligners with a straight extended design and selecting the appropriate aligner thickness.

Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering

IntroductionThe mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties. MethodsMulti-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.Results and discussionThe method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.

Effectiveness of electric vehicle subsidies in China: A three-dimensional panel study

Electric vehicles (EVs) are likely to emerge as the main means of zero-emission road transport. China has used a variety of policy approaches to encourage EV adoption, including vehicle purchase subsidies. This study uses a three-dimensional dataset to estimate the effect of purchase subsidies for domestic EVs on adoption in 316 cities in China over January 2016–December 2019. An instrumental variable approach that utilizes the timing of the cancellation of local subsidies by the central government is pursued. The findings suggest that purchase subsidies for domestic EVs have led to a sizeable increase in uptake, but have discouraged uptake of imported EVs. Higher consumer awareness of the subsidies is associated with a larger proportional effect on uptake of domestically-produced vehicles. We estimate that increases in the per-vehicle subsidy rate have on average reduced carbon dioxide (CO2) emissions at a marginal subsidy cost of about 4,453 CNY (US$712) per tonne, which is high. However other benefits, including long-run benefits from the emergence of a new clean technology sector, may be substantial.

Automated Midline Shift Detection and Quantification in Traumatic Brain Injury: A Comprehensive Review

AbstractTraumatic brain injury (TBI) often results in midline shift (MLS) that is a critical indicator of the severity and prognosis of head injuries. Automated analysis of MLS from head computed tomography (CT) scans using artificial intelligence (AI) techniques has gained much attention in the past decade and has shown promise in improving diagnostic efficiency and accuracy. This review aims to summarize the current state of research on AI-based approaches for MLS analysis in TBI cases, identify the methodologies employed, evaluate the performance of the algorithms, and draw conclusions regarding their potential clinical applicability. A comprehensive literature search was conducted, identifying 15 distinctive publications. The identified articles were analyzed for their focus on MLS detection and quantification using AI techniques, including their choice of AI algorithms, dataset characteristics, and methodology. The reviewed articles covered various aspects related to MLS detection and quantification, employing deep neural networks trained on two-dimensional or three-dimensional CT imaging datasets. The dataset sizes ranged from 11 patients' CT scans to 25,000 CT images. The performance of the AI algorithms exhibited variations in accuracy, sensitivity, and specificity, with sensitivity ranging from 70 to 100%, and specificity ranging from 73 to 97.4%. AI-based approaches utilizing deep neural networks have demonstrated potential in the automated detection and quantification of MLS in TBI cases. However, different researchers have used different techniques; hence, critical comparison is difficult. Further research and standardization of evaluation protocols are needed to establish the reliability and generalizability of these AI algorithms for MLS detection and quantification in clinical practice. The findings highlight the importance of AI techniques in improving MLS diagnosis and guiding clinical decision-making in TBI management.

Surface—subsurface grain structure relationships

Relationships between surface and subsurface grain structure are studied using a large three-dimensional (3D) microstructural dataset of a nickel-base superalloy Inconel 718. Comparative analyses of microstructural features were performed from both an orthographic, two-dimensional (2D) perspective and a full-3D one to underscore their distinctions on a grain-by-grain basis for a given surface microstructure. A positive correlation between grain size and the number of nearest neighbors of surface grains was observed; however, considerable variability exists per grain. We also present a detailed investigation of 3D surface grain structure and highlight the limitations of 2D surface grain characterizations. Several features of subsurface grain structure, including out-of-plane grain boundary inclination angle, surface-to-subsurface feature proximity, and subsurface grain boundary interface geometry are characterized. The analysis reveals a wide array of highly non-columnar subsurface grain structures with many points at the free surface being closer to grain boundaries and triple junctions beneath the surface than in the surface observation plane. Repeating these analyses for a synthetic dataset without twins reveals that our findings apply to other polycrystalline materials with near-equiaxed grains and limited texture.

Smart lattice light-sheet microscopy for imaging rare and complex cellular events.

Light-sheet microscopes enable rapid high-resolution imaging of biological specimens; however, biological processes span spatiotemporal scales. Moreover, long-term phenotypes are often instigated by rare or fleeting biological events that are difficult to capture with a single imaging modality. Here, to overcome this limitation, we present smartLLSM, a microscope that incorporates artificial intelligence-based instrument control to autonomously switch between epifluorescent inverted imaging and lattice light-sheet microscopy (LLSM). We apply this approach to two unique processes: cell division and immune synapse formation. In each context, smartLLSM provides population-level statistics across thousands of cells and autonomously captures multicolor three-dimensional datasets or four-dimensional time-lapse movies of rare events at rates that dramatically exceed human capabilities. From this, we quantify the effects of Taxol dose on spindle structure and kinetochore dynamics in dividing cells and of antigen strength on cytotoxic T lymphocyte engagement and lytic granule polarization at the immune synapse. Overall, smartLLSM efficiently detects rare events within heterogeneous cell populations and records these processes with high spatiotemporal four-dimensional imaging over statistically significant replicates.

Insights into the hydrogen evolution reaction in vanadium redox flow batteries: A synchrotron radiation based X-ray imaging study

The parasitic hydrogen evolution reaction (HER) in the negative half-cell of vanadium redox flow batteries (VRFBs) causes severe efficiency losses. Thus, a deeper understanding of this process and the accompanying bubble formation is crucial. This benchmarking study locally analyzes the bubble distribution in thick, porous electrodes for the first time using deep learning-based image segmentation of synchrotron X-ray micro-tomograms. Each large three-dimensional data set was processed precisely in less than one minute while minimizing human errors and pointing out areas of increased HER activity in VRFBs. The study systematically varies the electrode potential and material, concluding that more negative electrode potentials of −200 mV vs. reversible hydrogen electrode (RHE) and lower cause more substantial bubble formation, resulting in bubble fractions of around 15%–20% in carbon felt electrodes. Contrarily, the bubble fractions stay only around 2% in an electrode combining carbon felt and carbon paper. The detected areas with high HER activity, such as the border subregion with more than 30% bubble fraction in carbon felt electrodes, the cutting edges, and preferential spots in the electrode bulk, are potential-independent and suggest that larger electrodes with a higher bulk-to-border ratio might reduce HER-related performance losses. The described combination of electrochemical measurements, local X-ray micro-tomography, AI-based segmentation, and 3D morphometric analysis is a powerful and novel approach for local bubble analysis in three-dimensional porous electrodes, providing an essential toolkit for a broad community working on bubble-generating electrochemical systems.

A real-time SVM-based hardware accelerator for hyperspectral images classification in FPGA

Hyperspectral imaging can be conceptualized as a three-dimensional dataset of spectral information related to a particular landscape. Generally speaking, these are aerial photographs captured by Earth observation satellites. A useful analogy for a hyperspectral image is one of a cube formed with the image acquired along the X and Y axes and a third dimension of spectral bands of varying wavelengths. Given the wealth of data contained within these images, they have been employed in both civilian and military applications such as terrain recognition, urban development supervision, recognition of rare minerals, and various other objectives. The increased utilization of these images has garnered the interest of researchers striving to create solutions that may enable faster processing of the images via parallel processing. In this context, FPGA technology is an option capable of facilitating the implementation of such a system for observation satellites. This research is situated within this framework and aims to develop an FPGA-synthesized hardware accelerator to facilitate real-time hyperspectral image categorization. By taking this approach, hardware-specific solutions can be implemented for embedded applications that process hyperspectral images and can also be integrated with further image processing steps. The proposed accelerator was constructed based on an advanced algorithmic model, resulting in outcomes consistent with those generated by the software-based solution. The experimental results demonstrate that the engineered accelerator can attain a pixel classification time equal to or less than the pixel acquisition time, thus conforming to the real-time processing criteria concerning classification time. Further, the manufactured accelerator exhibits scalability that can classify distinct datasets with varying classes concurrently while maintaining a uniform logic resource utilization.

JSMNET: IMPROVING INDOOR POINT CLOUD SEMANTIC AND INSTANCE SEGMENTATION THROUGH SELF-ATTENTION AND MULTISCALE FUSION

Abstract. The semantic understanding of indoor 3D point cloud data is crucial for a range of subsequent applications, including indoor service robots, navigation systems, and digital twin engineering. Global features are crucial for achieving high-quality semantic and instance segmentation of indoor point clouds, as they provide essential long-range context information. To this end, we propose JSMNet, which combines a multi-layer network with a global feature self-attention module to jointly segment three-dimensional point cloud semantics and instances. To better express the characteristics of indoor targets, we have designed a multi-resolution feature adaptive fusion module that takes into account the differences in point cloud density caused by varying scanner distances from the target. Additionally, we propose a framework for joint semantic and instance segmentation by integrating semantic and instance features to achieve superior results. We conduct experiments on S3DIS, which is a large three-dimensional indoor point cloud dataset. Our proposed method is compared against other methods, and the results show that it outperforms existing methods in semantic and instance segmentation and provides better results in target local area segmentation. Specifically, our proposed method outperforms PointNet (Qi et al., 2017a) by 16.0% and 26.3% in terms of semantic segmentation mIoU in S3DIS (Area 5) and instance segmentation mPre, respectively. Additionally, it surpasses ASIS (Wang et al., 2019) by 6.0% and 4.6%, respectively, as well as JSPNet (Chen et al., 2022) by a margin of 3.3% for semantic segmentation mIoU and a slight improvement of 0.3% for instance segmentation mPre.

Hyperspectral Image Analysis using LSTM and 2D CNN and its Application in Remote Sensing

The land cover classification in urban areas is described in this research work. The use of hyperspectral image analysis is growing in popularity because it performs better than conventional machine learning techniques. Hypercubes, a type of three-dimensional dataset with two spatial dimensions and one spectral dimension, make up the Hyperspectral imaging (HSI). An overview of HSI's uses in remote sensing applications and the methods for classifying it are given in this research. In the field of HSI, numerous experiments are conducted with various deep learning methods for analysis and classification. The main components of this research is convolutional neural network (CNN)and long short-term memory (LSTM) that shows to be more effective than alternative models. In this case, spectral and spatial features are extracted using CNN and LSTM, respectively, and the results are then classified using support vector machines (SVM). The datasets utilized in this study were gathered using a ROSIS sensor/spectrometer at Pavia University and Indian Pines.

Joint 2D–3D cross-pseudo supervision for carotid vessel wall segmentation

IntroductionThe segmentation of the carotid vessel wall using black-blood magnetic resonance images was a crucial step in the diagnosis of atherosclerosis. The objective was to accurately isolate the region between the artery lumen and outer wall. Although supervised learning methods achieved remarkable accuracy in vessel segmentation, their effectiveness remained limited due to their reliance on extensive labeled data and human intervention. Furthermore, when confronted with three-dimensional datasets featuring insufficient and discontinuous label data, these learning-based approaches could lose their efficacy. In this paper, we proposed a novel Joint 2D–3D Cross-Pseudo Supervision (JCPS) method for accurate carotid vessel wall segmentation.MethodsIn this study, a vascular center-of-gravity positioning module was developed to automatically estimate the region of blood vessels. To achieve accurate segmentation, we proposed a joint 2D–3D semi-supervised network to model the three-dimensional continuity of vascular structure. In addition, a novel loss function tailored for vessel segmentation was introduced, consisting of four components: supervision loss, cross-pseudo supervision loss, pseudo label supervision loss, and continuous supervision loss, all aimed at ensuring the accuracy and continuity of the vessel structure. In what followed, we also built up a user-friendly Graphical User Interface based on our JCPS method for end-users.ResultsOur proposed JCPS method was evaluated using the Carotid Artery Vessel Wall Segmentation Challenge dataset to assess its performance. The experimental results clearly indicated that our approach surpassed the top 10 methods on the leaderboard, resulting in a significant enhancement in segmentation accuracy. Specifically, we achieved an average Dice similarity coefficient increase from 0.775 to 0.806 and an average quantitative score improvement from 0.837 to 0.850, demonstrating the effectiveness of our proposed JCPS method for carotid artery vessel wall segmentation.ConclusionThe experimental results suggested that the JCPS method had a high level of generalization performance by producing pseudo labels that were comparable with software annotations for data-imbalanced segmentation tasks.

3D surgical instrument collection for computer vision and extended reality

The availability of computational hardware and developments in (medical) machine learning (MML) increases medical mixed realities’ (MMR) clinical usability. Medical instruments have played a vital role in surgery for ages. To further accelerate the implementation of MML and MMR, three-dimensional (3D) datasets of instruments should be publicly available. The proposed data collection consists of 103, 3D-scanned medical instruments from the clinical routine, scanned with structured light scanners. The collection consists, for example, of instruments, like retractors, forceps, and clamps. The collection can be augmented by generating likewise models using 3D software, resulting in an inflated dataset for analysis. The collection can be used for general instrument detection and tracking in operating room settings, or a freeform marker-less instrument registration for tool tracking in augmented reality. Furthermore, for medical simulation or training scenarios in virtual reality and medical diminishing reality in mixed reality. We hope to ease research in the field of MMR and MML, but also to motivate the release of a wider variety of needed surgical instrument datasets.

Generalized black hole clustering algorithm

The Black Hole Clustering (BHC) algorithm is a density-based partitional clustering method inspired by the Density-based Spatial Clustering of Applications with Noise (DBSCAN). It does not require the number of clusters nor the computation of the pair-wise distance matrix between the data points, making it faster than DBSCAN. Also, it only needs one parameter that is intuitively easier to set than the epsilon parameter of DBSCAN. However, BHC needs the allocation of the so-called black holes that have to be linearly independent, making the algorithm in its current version suitable only for two or three-dimensional data sets. In this paper, we propose a generalized version of the black hole clustering algorithm (GBHC) by introducing a novel black hole allocation procedure for higher-dimensional data spaces. Furthermore, the proposed method is data-independent, so we have to run it once to obtain the black hole positions for all finite-dimensional metric spaces. We performed extensive computational experiments to compare GBHC with DBSCAN. The results show that both algorithms obtain comparable clustering solutions. GBHC, however, outperforms DBSCAN in computational complexity and explainability.

Grain-size variability in debris flows of different runout lengths, Wenchuan, China

Debris-flow grain-size distributions (GSDs) control runout length and mobility. Wide, bimodal GSDs and those containing a higher proportion of silt and clay have been shown experimentally to increase runout length. However, the relationship between grain size and mobility has not been well established in field conditions. Here, we compared the grain-size characteristics of two debris flows with considerably different runout lengths (1.5 km vs. 8 km) to understand the role of grain size in governing runout. The two debris flows were triggered in the same rainfall event from coseismic landslide debris generated in the 2008 Wenchuan earthquake in catchments with similar lithology and topography. We compared the deposited GSDs and their spatial patterns using our rare, three-dimensional GSD datasets. Surprisingly, the proportions of each size fraction deposited by the two flows were statistically indistinguishable. The spatial pattern of grain size differed between the two flows, with evidence of inverse grading only preserved in the smaller deposit. From these observations, we can infer that the GSDs of both flows were determined by the coseismic landslide source material, and that there was little difference in the GSDs of material entrained as the flows bulked. The contrasting spatial distributions of grains indicated that different internal processes were dominant within the two flows. These findings demonstrate that where GSDs are dominated by coarse grains and are governed by similar source conditions, grain size plays a lesser role relative to sediment supply and hydrology in controlling the runout length of large catastrophic post-earthquake debris flows.