Three-dimensional Data Research Articles

The gait pattern and not the femoral morphology is the main contributor to asymmetric hip joint loading.

Gait asymmetry and skeletal deformities are common in many children with cerebral palsy (CP). Changes of the hip joint loading, i.e. hip joint contact force (HJCF), can lead to pathological femoral growth. A child's gait pattern and femoral morphology affect HJCFs. The twofold aim of this study was to (1) evaluate if the asymmetry in HJCFs is higher in children with CP compared to typically developing (TD) children and (2) identify if the bony morphology or the subject-specific gait pattern is the main contributor to asymmetric HJCFs. Magnetic resonance images (MRI) and three-dimensional gait analysis data of twelve children with CP and fifteen TD children were used to create subject-specific musculoskeletal models and calculate HJCF using OpenSim. Root-mean-square-differences between left and right HJCF magnitude and orientation were computed and compared between participant groups (CP versus TD). Additionally, the influence on HJCF asymmetries solely due to the femoral morphology and solely due to the gait pattern was quantified. Our findings demonstrate that the gait pattern is the main contributor to asymmetric HJCFs in CP and TD children. Children with CP have higher HJCF asymmetries which is probably the result of larger asymmetries in their gait pattern compared to TD children. The gained insights from our study highlight that clinical interventions should focus on normalizing the gait pattern and therefore the hip joint loading to avoid the development of femoral deformities.

Multiband seizure type classification based on 3D convolution with attention mechanisms

Electroencephalogram (EEG) signal contains important information about abnormal brain activity, which has become an important basis for epilepsy diagnosis. Recently, epilepsy EEG signal classification methods mainly extract features from the perspective of a single domain, which cannot effectively utilize the spatial domain information in EEG signals. The redundant information in EEG signals will affect the learning features with the increase of convolution layer and multi-domain features, resulting in inefficient learning and a lack of distinguishing features. To tackle these issues, we propose an end-to-end 3D convolutional multiband seizure-type classification model based on attention mechanisms. Specifically, to process preprocessed electroencephalogram (EEG) data, a multilevel wavelet decomposition is applied to obtain the joint distribution information in the two-dimensional time–frequency domain across multiple frequency bands. Subsequently, this information is transformed into three-dimensional spatial data based on the electrode configuration. Discriminative joint activity features in the time, frequency, and spatial domains are then extracted by a series of parallel 3D convolutional sub-networks, where 3D channels and spatial attention mechanisms improve the ability to learn critical global and local information. A multi-layer perceptron is finally implemented to integrate the extracted features and further map them to the classification results. Experimental results on the TUSZ dataset, the world’s largest publicly available seizure corpus, show that 3D-CBAMNet significantly outperforms the state-of-the-art methods, indicating effectiveness in the seizure type classification task.

Inclined angles of acetabular quadrilateral plate: digital measurement and clinical application of the new anatomical concept

PurposeMatta scoring standard is one of the most frequently used postoperative imaging evaluations for acetabular fracture reduction, but has obvious shortcomings. This study, for the first time, proposed the concept of inclined angles of acetabular quadrilateral plate. The purpose of this study was to investigate the normal range of the inclined angles in adults by digital measurement and explore the feasibility of using them to evaluate the reduction quality of acetabular quadrilateral fractures after surgery.MethodsFirstly, the pelvic CT three-dimensional reconstruction data of 40 healthy adults including 20 males and 20 females were collected. The normal range of the anterior, middle, and posterior inclined angles were measured via Mimics software. Secondly, a modified Matta criteria that combined the classic Matta criteria and evaluation criteria of the inclined angles was proposed. And we classified modified Matta criteria into three grades including excellent, good and poor. Finally, a total of 125 cases with quadrilateral plate fractures was included and the postoperative CT data were analyzed by using both the classic Matta criteria and our modified Matta criteria. Then, the accuracy and consistency of both criteria to evaluate postoperative hip function was investigated.ResultsThe average anterior inclined angle: male (97.11° ± 2.59°), female (90.63° ± 2.09°); middle inclined angle: male (105.57° ± 1.93°), female (100.64° ± 2.46°); and posterior inclined angle: male (112.62° ± 2.54), female (106.37° ± 2.53°). Whether in males or in females, the anterior, middle, and posterior inclined angles showed a progressively increasing trend. All the three inclined angles in males were all significantly larger than those in females (p < 0.05). Among 125 cases with quadrilateral plate fractures, 101 cases (80.8%) were graded as excellent, 18 cases (14.4%) as good, and 6 cases (4.8%) as poor according to the classic Matta criteria. While based on modified Matta criteria, there were excellent in 37 cases (29.6%), good in 76 cases (60.8%), and poor in 12 cases (9.6%). According to the Harris hip score system, the functional outcomes were excellent in 59 cases (47.2%), good in 26 cases (20.8%), fair in 24 cases (19.2%), and poor in 16 cases (12.8%). Our results showed that among the cases evaluated as excellent according to the classic Matta criteria and modified Matta criteria, the excellent-to-good rates of hip function were 70.3% and 78.4%, respectively. And among the cases evaluated as poor according to the modified Matta criteria, the fair-to-poor rate of hip function was 75%, while this value was 50% for classic Matta criteria. Both differences were statistically significant (p < 0.05).ConclusionInclined angles of the quadrilateral plate could be used to assess the quality of fracture reduction and provide a basis for evaluating the rotational displacement of fracture blocks in the quadrilateral plate, which compensates the shortage of classic Matta criteria.

Research and Implementation of Three-Dimensional Spatial Information Characterization and Visualization of Fractures in Deteriorated Sandstone

Primary fractures significantly impacted the stability of surrounding rock in underground projects. Therefore, it is vital to find a solution for the problem of performing a non-destructive detection of rocks and extracting the internal three-dimensional (3D) data field of rocks for visualization analysis. To address this problem, this paper proposed a method of 3D reconstruction for complex cracks in deteriorated sandstone and developed a program based on MATLAB. This work carried out image recognition on the CT scan images of deteriorated sandstone, then implemented a surface reconstruction technique based on object cross-section information, a contour reconstruction technique based on object contour information, a point cloud reconstruction technique for extracting point cloud data of internal cracks in deteriorated sandstone, and a Graphical User Interface (GUI) control system that combines these three reconstruction techniques. The results showed that the 3D reconstruction techniques and the GUI control system proposed in this paper were capable of precisely marking the location of the cracks on a 3D coordinate system and accurately describing their shape with a vector. With only 10 CT scan images, the point cloud reconstruction technique constructed the digital core, and the digital core can quantitatively characterize the influence of primary fractures on the stability of surrounding rock. Additionally, the calculated results of the proposed method were very close to that of Avizo. This method realized the visualization and quantitative characterization of the internal structure of rocks and offered a model for analyzing the stress-fracture-seepage field change during excavation.

A Dual Blind Watermarking Method for 3D Models Based on Normal Features.

Digital watermarking technology is an important means to effectively protect three-dimensional (3D) model data. Among them, "blind detection" and "robustness" are key and difficult points in the current research of digital watermarking technology based on 3D models. In order to realize the blind detection of a watermark and improve its robustness against various common attacks at the same time, this paper proposes a dual blind watermarking method based on the normal feature of the centroid of first-ring neighboring points. The local spherical coordinate system is constructed by calculating two different normal vectors, and the first pattern watermark and the second random binary sequence watermark are embedded, respectively. The experimental results show that this method can not only realize the blind detection of dual watermarks, but also have the ability to resist common attacks such as translation, rotation, scaling, cropping, simplification, smoothing, noise, and vertex reordering to a certain extent.

Clinical validation of an AI-based automatic quantification tool for lung lobes in SPECT/CT

BackgroundLung lobar ventilation and perfusion (V/Q) quantification is generally obtained by generating planar scintigraphy images and then imposing three equally sized regions of interest on the data of each lung. This method is fast but not as accurate as SPECT/CT imaging, which provides three-dimensional data and therefore allows more precise lobar quantification. However, the manual delineation of each lobe is time-consuming, which makes SPECT/CT incompatible with the clinical workflow for V/Q estimation. An alternative may be to use artificial intelligence-based auto-segmentation tools such as AutoLung3D (Siemens Healthineers, Knoxville, USA), which automatically delineate the lung lobes on the CT data acquired with the SPECT data. The present study assessed the clinical validity of this approach relative to planar scintigraphy and manual quantification in SPECT/CT.MethodsThe Autolung3D software was tested on the retrospective SPECT/CT data of 43 patients who underwent V/Q scintigraphy with 99mTc-macroaggregated albumin and 99mTc-labeled aerosol. It was compared to planar scintigraphy and SPECT/CT using the manual quantification method in terms of relative lobar V/Q quantification values and interobserver variability.ResultsThe three methods provided similar V/Q estimates for the left lung lobes and total lungs. However, compared to the manual SPECT/CT method, planar scintigraphy yielded significantly higher estimates for the middle right lobe and significantly lower estimates for the superior and inferior right lobes. The estimates of the manual and automated SPECT/CT methods were similar. However, the post-processing time in the automated method was approximately 5 min compared to 2 h for the manual method. Moreover, the automated method associated with a drastic reduction in interobserver variability: Its maximal relative standard deviation was only 5%, compared to 23% for planar scintigraphy and 19% for the manual SPECT/CT method.ConclusionsThis study validated the AutoLung3D software for general clinical use since it rapidly provides accurate lobar quantification in V/Q scans with markedly less interobserver variability than planar scintigraphy or the manual SPECT/CT method.

Adapting to Altered Sensory Input: Effects of Induced Paresthesia on Goal-Directed Movement Planning and Execution.

The current study investigated how temporarily induced paresthesia in the moving limb affects the performance of a goal-directed target aiming task. Three-dimensional displacement data of 14 neurotypical participants were recorded while they pointed to a target on a computer monitor in four conditions: (i) paresthesia-full-vision; (ii) paresthesia-without-target vision; (iii) no-paresthesia-full-vision; (iv) no paresthesia-without-target vision. The four conditions were blocked and counterbalanced such that participants performed the paresthesia and no-paresthesia conditions on two separate days. To assess how aiming performance changed in the presence of paresthesia, we compared early versus late performance (first and last 20% of trials). We found that endpoint accuracy and movement speed were reduced in the presence of paresthesia, but only without target vision. With repetition, participants adjusted their movement performance strategy, such that with induced paresthesia, they used a movement strategy that included more pre-planned movements that depended less on online control.

Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait

Markerless motion capturing has the potential to provide a low-cost and accessible alternative to traditional marker-based systems for real-world biomechanical assessment. However, before these systems can be put into practice, we need to rigorously evaluate their accuracy in estimating joint kinematics for various gait patterns. This study evaluated the accuracy of a low-cost, open-source, and smartphone-based markerless motion capture system, namely OpenCap, for measuring 3D joint kinematics in healthy and pathological gait compared to a marker-based system. 21 healthy volunteers were instructed to walk with four different gait patterns: physiological, crouch, circumduction, and equinus gait. Three-dimensional kinematic data were simultaneously recorded using the markerless and a marker-based motion capture system. The root mean square error (RMSE) and the peak error were calculated between every joint kinematic variable obtained by both systems. We found an overall RMSE of 5.8 (SD: 1.8 degrees) and a peak error of 11.3 degrees (SD: 3.9). A repeated measures ANOVA with post hoc tests indicated significant differences in RMSE and peak errors between the four gait patterns (p ¡ 0.05). Physiological gait presented the lowest, crouch and circumduction gait the highest errors. Our findings indicate a roughly comparable accuracy to IMU-based approaches and commercial markerless multi-camera solutions. However, errors are still above clinically desirable thresholds of two to five degrees. While our findings highlight the potential of markerless systems for assessing gait kinematics, they also underpin the need to further improve the underlying deep learning algorithms to make markerless pose estimation a valuable tool in clinical settings.

Study on the Measurement Method of Wheat Volume Based on Binocular Structured Light

In this paper, we propose a grain volume measurement method based on binocular structured light to address the need for fast and high-precision grain volume measurement in grain stocks. Firstly, we utilize speckle structured light imaging to tackle the image matching problem caused by non-uniform illumination in the grain depot environment and the similar texture of the grain pile surface. Secondly, we employ a semi-global stereo matching algorithm with census transformation to obtain disparity maps in grain bins, which are then converted into depth maps using the triangulation principle. Subsequently, each pixel in the depth map is transformed from camera coordinates to world coordinates using the internal and external parameter information of the camera. This allows us to construct 3D cloud data of the grain pile, including the grain warehouse scene. Thirdly, the improved European clustering method is used to achieve the segmentation of the three-dimensional point cloud data of the grain pile and the scene of the grain depot, and the pass-through filtering method is used to eliminate some outliers and poor segmentation points generated by segmentation to obtain more accurate three-dimensional point cloud data of the grain pile. Finally, the improved Delaunay triangulation method was used to construct the optimal topology of the grain surface continuous triangular mesh, and the nodes of the grain surface triangular mesh were projected vertically to the bottom of the grain warehouse to form several irregular triangular prisms; then, the cut and complement method was used to convert these non-plane triangular prisms into regular triangular prisms that could directly calculate the volume. The measured volume of the pile is then obtained by calculating the volume of the triangular prism. The experimental results indicate that the measured volume has a relative error of less than 1.5% and an average relative error of less than 0.5%. By selecting an appropriate threshold, the relative standard deviation can be maintained within 0.6%. The test results obtained from the laboratory test platform meet the requirements for field inspection of the granary.

Classification of rock slope cavernous weathering on UAV photogrammetric point clouds: The example of Hegra (UNESCO World Heritage Site, Kingdom of Saudi Arabia)

The analysis of three-dimensional point cloud data is becoming one of the most used approaches to assess instabilities processes affecting rock slopes. With the increased collection of point cloud data, there is an increasing demand for rapid computational point cloud segmentation techniques to format data for rock fall risk analysis. However, there is a scarcity of semantic segmentation research focusing on geological data and the practical issues they pose. In this study, we propose a novel application to automatically define the spatial extent and intensity of weathered rock slope areas. The studied rock slopes are in Hegra Archaeological Site (al-Hijr / Madā ͐ in Ṣāliḥ), the first UNESCO World Heritage area inscribed in the Kingdom of Saudi Arabia (KSA) in 2008. Here, cavernous weathering processes, with honeycombs and tafoni features, affect the carved rock slope and increase the rockfall hazard. With the spread and improvement of close-range methods, such as Terrestrial Laser Scanner (TLS) and Unmanned Aerial Vehicle Digital Photogrammetry (UAV-DP), it is possible to obtain fast and accurate high-resolution point cloud data of the studied scenario, i.e., rock mass. This paper presents the automated mapping results obtained by a combination of explainable artificial intelligence (XAI) algorithms and three-dimensional geometric analysis of a point cloud using the geometric feature tools available for free on CloudCompare (CC) open-source software. Three-dimensional data were obtained using the UAV-DP technique. The proposed workflow allows, using random forest modelling (RF), to quantify the cavernous weathering extent and estimate the relative intensity on the rock slope, faster than using traditional time-consuming manual segmentation processes. The RF model was able to accurately forecast the cavernous weathering regions (accuracy of up to 85%), with a slight propensity to underpredict, as indicated by the somewhat low recall (∼ 63%), and a limited number of false positives, as indicated by the high precision (up to 84%). This approach is easily reproducible at low cost in terms of computational, human, and financial resources. The results highlight the reliability of the proposed method for the preliminary assessment of cavernous erosional features with the aim of future assessment, monitoring, and deeper analysis of rock-carved slopes.

Renal tubular function and morphology revealed in kidney without labeling using three-dimensional dynamic optical coherence tomography

Renal tubule has distinct metabolic features and functional activity that may be altered during kidney disease. In this paper, we present label-free functional activity imaging of renal tubule in normal and obstructed mouse kidney models using three-dimensional (3D) dynamic optical coherence tomography (OCT) ex vivo. To create an obstructed kidney model, we ligated the ureter of the left kidney for either 7 or 14 days. Two different dynamic OCT (DOCT) methods were implemented to access the slow and fast activity of the renal tubules: a logarithmic intensity variance (LIV) method and a complex-correlation-based method. Three-dimensional DOCT data were acquired with a 1.3 upmum swept-source OCT system and repeating raster scan protocols. In the normal kidney, the renal tubule appeared as a convoluted pipe-like structure in the DOCT projection image. Such pipe-like structures were not observed in the kidneys subjected to obstruction of the ureter for several days. Instead of any anatomical structures, a superficial high dynamics appearance was observed in the perirenal cortex region of the obstructed kidneys. These findings suggest that volumetric LIV can be used as a tool to investigate kidney function during kidney diseases.

“Geology-geophysics-data mining” integration to enhance the identification of deep fault-controlled paleokarst reservoirs in the Tarim Basin

Large-scale strike-slip faults play a dual role as conduits for fluid flow, channeling both atmospheric freshwater and deep hydrothermal fluids, thereby creating porous carbonate reservoirs at significant depths. Moreover, these faults serve as vertical conduits, connecting deep source rocks to oil and gas reservoirs capable of generating substantial hydrocarbon fields. In recent years, the Tarim Basin has yielded numerous high-yield reservoirs along large-scale strike-slip faults, exhibiting exceptional hydrocarbon potential. Nevertheless, the intricate heterogeneity of deep-seated traps engendered by these faults presents a formidable challenge in accurately determining reservoir geometry, consequently complicating the drilling of high-yield wells. Currently, enhancing the precision of reservoir identification stands as a paramount issue within the Tarim Basin.This paper introduces an innovative solution termed the “Geology-Geophysics-Data Mining” workflow, designed to enhance the resolution of multiscale geophysics data through the lens of geological insights. Drawing inspiration from the geological model of “fault-controlled paleokarst,” and utilizing a fusion of well logs and seismic data, we elect seismic impedance as a discerning parameter to differentiate between porous reservoirs and impermeable host rocks. The proposed methodology unfolds through several steps.Primarily, noise reduction and frequency domain extension of the original seismic data are achieved through spectral shaping and diffusion filtering algorithms. Subsequently, the impedance curve of the logging wave is computed, establishing a non-linear correlation with seismic waveforms using a waveform indication inversion algorithm, ultimately yielding inverse impedance data. In the subsequent phase, the impedance originating from stable sedimentary features is quantified and subsequently extracted from the inversely derived seismic data. This step accentuates the high-quality reservoirs formed via fault-controlled karstification. Ultimately, we validate the identification of reservoir geometry using interpretations from well log data and drilling outcomes.Furthermore, this paper employs the seismic impedance obtained from wellbore logging data as “hard data” and deploys machine learning algorithms to map this information to three-dimensional high-precision seismic data. Following the removal of sedimentary features' influence, we extract the longitudinal connectivity and transverse mutation characteristics inherent in fault-controlled paleokarst reservoirs. This extraction enables the capture of geometric structural information related to fault-controlled paleokarst reservoirs, further substantiating the geological distribution of effective reservoirs controlled by strike-slip faults. These findings establish a dependable foundation for directional drilling and studies focused on hydrocarbon accumulation. Notably, the “Geology-Geophysics-Data Mining” approach is extendable to analogous strongly heterogeneous reservoirs.

Automated extraction of biplanar stereo-radiographic image measurements: Mizzou 3D SPinE.

Although several studies have reported on the application of biplanar stereo-radiographic technology in pediatric clinical practice, few have performed large-scale analyses. The manual extraction of these types of data is time-consuming, which often precludes physicians and scientists from effectively utilizing these valuable measurements. To fill the critical gap between clinical assessments and large-scale evidence-based research, we have addressed one of the primary hurdles in using data derived from these types of imaging modalities in pediatric clinical practice by developing an application to automatically transcribe and aggregate three-dimensional measurements in a manner that facilitates statistical analyses. Mizzou 3D SPinE was developed using R software; the application, instructions, and process were beta tested with four separate testers. We compared 1309 manually compiled three-dimensional deformity measurements derived from thirty-five biplanar three-dimensional reconstructions (image sets) from ten pediatric patients to those derived from Mizzou 3D SPinE. We assessed the difference between manually entered values and extracted values using a Fisher's exact test. Mizzou 3D SPinE significantly reduced the duration of data entry (95.8%) while retaining 100% accuracy. Manually compiled data resulted in an error rate of 1.58%, however, the magnitude of errors ranged from 5.97 to 2681.82% significantly increased the transcription accuracy (p value < 0.0001) while also significantly reducing transcription time (0.33 vs. 8.08min). Mizzou 3D SPinE is an essential component in improving evidence-based patient care by allowing clinicians and scientists to quickly compile three-dimensional data at regular intervals in an automated, efficient manner without transcription errors.

Sex differences regarding the effects of arm swing on ground reaction force and trunk movements between track and field athletes

The purpose of this study was to clarify the sex difference in the effects of arm swing movements of track and field athletes on ground reaction force and trunk movement. Seven male and nine female athletes belonging to a university track and field team were asked to perform arm swings for 10 seconds each under three different conditions (longitudinal, lateral, and original) while in the standing position. Three-dimensional coordinate data for each experimental trial was collected using an automatic optical motion analyser, and ground reaction forces were measured using a force plate. Under the longitudinal condition, the mean acceleration force was greater for males than for females (p &lt; .05), and the operating range of trunk twist angle was significantly greater for females than for males (p &lt; .05). However, under the original condition, there were no significant differences between the sexes in mean acceleration force, but there were significant differences in maximum twist angle and minimum and maximum shoulder abduction angles (p &lt; .05). These results indicate that there are sex differences in trunk movement and ground reaction force depending on the direction of arm swing.

Real-time localization and 3D semantic map reconstruction for unstructured citrus orchards

Semantic maps play a crucial role in smart agriculture, providing practical three-dimensional fruit tree data for orchard management and aiding the optimization of management strategies and improvement of economic benefits. However, previous map studies have mainly focused on geometric features and have lacked semantic information, limiting robots' ability to reason about useful information in complex tasks and achieve human–machine interaction. Furthermore, most existing map reconstructions have been offline or nonreal-time, making it difficult to satisfy the needs of real-time decision-making and planning in agricultural scenarios. Therefore, this paper proposes a real-time localization and semantic map reconstruction method for unstructured citrus orchards, integrating the visual-inertial SLAM VINS-RGBD framework with the semantic segmentation algorithm BiSeNetV1. By conducting semantic segmentation of 2D RGB images and mapping them to point clouds, a 3D semantic point cloud map is reconstructed. The statistical outlier removal filter and OctoMap are introduced for postprocessing to remove outliers and estimate obstacles in 3D space, constructing a more accurate, efficient and flexible map. The experimental results show that the proposed method achieved a semantic segmentation accuracy mIoU of 79.31% on a self-built citrus dataset, a citrus recall relative error of 11.29% and a localization accuracy mean translational error of 1.917 m with the map constructed under an unstructured orchard scenario. Additionally, the average memory saving rate of the statistical outlier removal filter was 10.36%, and the average memory saving rate of OctoMap was 97.39%. The processing time for each frame of real-time front-end feature detection and tracking was 11.14 ms. Moreover, the deployed semantic segmentation network BiSeNetV1 achieved a processing time of 7.35 ms per frame. These results indicate that the proposed method can achieve both high accuracy and real-time performance in semantic map reconstruction. This exploratory work provides theoretical and technical references for future research on more precise localization and more complete semantic mapping and has extensive application potential, providing essential technical support for intelligent agriculture.

An instructional workflow for using terrestrial laser scanning (TLS) to quantify vegetation structure for wildlife studies

Vegetation structure is a crucial component of habitat suitability for wildlife, but methods traditionally used to quantify it can be costly, subjective, or difficult to replicate. There have been substantial advancements, and growing interest, in the use of terrestrial laser scanning (TLS) methods to measure fine-scale vegetation attributes in ecological research, but limited opportunities for ecologists and practitioners to learn how to incorporate these into applied research. Here, we provide a starting point for those who want to incorporate TLS methods into ecological research and monitoring, using a case study from a temperate forest in South-Eastern Australia. We scanned 24 sites at different stages of post-fire recovery as part of a larger project measuring the impact of fire on forest dwelling bats, using a FARO Focus 3DS. Using the data from these scans, we present an example workflow with accompanying R code to demonstrate how to process the point clouds and extract a range of vegetation structure metrics relevant to habitat structure for wildlife, including vegetation height, cover, density, and heterogeneity. We discuss how three-dimensional data obtained through TLS can be valuable to wildlife studies and opens up the potential to explore advanced ecological and conservation questions around how vegetation structure influences wildlife behaviours, distribution patterns, and responses to disturbance.

P11.16.B THE MAYO CLINIC MULTIDISCIPLINARY NF2 CLINIC EXPERIENCE OF BEVACIZUMAB IN NEUROFIBROMATOSIS TYPE 2-RELATED SCHWANNOMATOSIS PATIENTS WITH VESTIBULAR SCHWANNOMAS

Abstract BACKGROUND Neurofibromatosis type 2-related schwannomatosis (NF2) is an autosomal dominant tumor-predisposition syndrome characterized by bilateral vestibular schwannomas (VS) as well as other nervous system tumors. Management decisions for NF2-related VS are typically predicated on tumor size, associated symptoms, patient age, and comorbidities. Observation, surgical resection, and stereotactic radiosurgery can be viable options. In patients with VS associated with NF2, vascular endothelial growth factor A (VEGF-A) inhibitor, bevacizumab, is a systemic treatment option. In this single institution study, we review our clinical experience in patients with NF2 who received bevacizumab for VS and evaluate the effect of bevacizumab treatment on VS growth and symptom progression. MATERIAL AND METHODS This is a retrospective analysis of patients seen at the Mayo Clinic Rochester Multidisciplinary NF2 Clinic. Three-dimensional volumetric data for VS were measured using manual segmentation with Visage PACS software under the direct supervision of a board-certified neuroradiologist. RESULTS Out of 76 patients with NF2 evaluated in the past 3 years, we identified 19 that received treatment with bevacizumab. Median age of NF2 diagnosis was 19 years (range 0.5-61) with a median NF2 phenotype severity score of 2 (range 1-3). Thirteen of these patients have discontinued bevacizumab, accounting for a median duration of 12.2 months (range 3.3-88 months) of bevacizumab treatment. Bevacizumab was discontinued due to hypertension (n=5), proteinuria and renal dysfunction (n=3), tumor progression (n=2), fatigue or other intolerance (n=2), or non-compliance (n=1). The remaining 6 patients are currently receiving bevacizumab treatment for a median duration of 9.4 months as of March 2023 (range 3.7-48.3 months). Fifteen patients had brain MRI data available to evaluate VS change during bevacizumab treatment, with partial response observed in 5 patients, stable disease in 8, and progression in 2. Ten of the 13 patients who stopped bevacizumab had MRI data to evaluate VS change following treatment termination. Within 6 months of bevacizumab discontinuation, 6 patients had rebound growth of their VS between 21-95% from their previous tumor volume, while 4 did not. Three patients with rebound growth went on to have surgery or irradiation for VS management. CONCLUSION Our single institution experience confirms that bevacizumab can slow progression of VS and symptoms associated with VS growth. However, we note that there is the potential for rapid VS growth following bevacizumab discontinuation, for which we propose heightened surveillance imaging and symptom monitoring for at least 6 months upon stopping anti-VEGF therapy.

Fossils in a Digital Age

Digital libraries are a powerful tool for preserving and disseminating information in a technology-driven world. Providing options for housing non-traditional information, such as three-dimensional (3D) data, digital libraries also address barriers (e.g., geographic location and physical space) that affect traditional libraries. While there are certainly still barriers that may prevent users from accessing and utilizing digital content, digital libraries offer ways of preserving, housing, and disseminating non-traditional information that physical libraries cannot. Within the anthropological community, digital libraries can serve as a method of housing 3D fossil data. There are unique challenges and barriers associated with studying fossils that digital libraries help to break down. Through the preservation and dissemination of 3D fossil data, researchers across the globe can work with fossil data without the concerns associated with handling fragile fossils. However, such 3D digital libraries are not without challenges. While there are clear standards and metadata schemas (e.g., RDA and DCMI) for digital libraries containing traditional materials, no such standardization exists for digital libraries containing 3D data. As such, metadata requirements and elements for existing 3D digital libraries are inconsistent and may lack important details about the data, which can compromise functionality. This paper explores trends and challenges that digital libraries containing 3D data experience and highlights best-practice solutions for future 3D digital libraries to overcome these challenges.

Mapping site index in coniferous forests using bi-temporal airborne laser scanning data and field data from the Swedish national forest inventory

Recent advancements in remote sensing of forests have demonstrated the capabilities of three-dimensional data acquired by airborne laser scanning (ALS) and, consequently, have become an integral part of enhanced forest inventories in Northern Europe. In Sweden, the first national laser scanning revolutionised forest management planning through low-cost production of large-scale and spatially explicit maps of forest attributes such as basal area, volume, and biomass, compared to the earlier practice based on field survey data. A second scanning at the national level was launched in 2019, and it provides conditions for the estimation of height growth and site index. Accurate and up-to-date information about site productivity is relevant for planning silvicultural treatments and for the prognosis of forest status and development over time. In this study, we explored the potential of bi-temporal ALS data and other auxiliary information to predict and map site productivity by site index according to site properties (SIS) of Norway spruce (Picea abies (L.) Karst) and Scots pine (Pinus sylvestris L.) in even-aged stands in Sweden. We linked ground survey data of SIS from more than 11,500 plots of the Swedish National Forest Inventory (NFI) to bi-temporal ALS data to predict and map site index using an area-based method and two regression modelling strategies: (1) a multiple linear regression (MLR) model with an ordinary least-squares parameter estimation method, and (2) a non-parametric random forests (RF) model optimised for hyper-parameter tuning. For model development, permanent plots were used, whereas the validation was done on the temporary plots of the Swedish NFI and an independent stand-level dataset. Species-specific models were developed, and the root mean square error (RMSE) metric was used to quantify the residual variability around model predictions. For both species, the MLR model gave precise and accurate estimates of SIS. The RMSE for SIS predictions was in the range of 1.96 – 2.11 m, and the relative RMSE was less than 10 % (7.68 – 9.49 %) of the reference mean value. Final predictors of site index include metrics of 90th percentile height and annual increment in the 95th percentile height, altitude, distance to coast, and soil moisture. Country-wide maps of SIS and the corresponding pixel-level prediction errors at a spatial resolution of 12.5 m grid cells were produced for the two species. Independent validations show the site index maps are suitable for use in operational forest management planning in Sweden.

Curbside Parking Monitoring With Roadside LiDAR

Cities worldwide are striving to find more efficient approaches to address the prevalent parking challenges in urban areas. A key aspect of achieving an optimal parking environment is the collection of curbside parking data, which enables informed decision-making and effective management of on-street parking spaces. This study proposes a solution for curbside parking monitoring and data collection using roadside LiDAR systems. By leveraging laser beam variation detection, this solution can extract essential information about parking usage. Unlike existing solutions, such as imagery or embedded sensor-based monitoring, our solution offers portability and ease of deployment for short-term or long-term curbside parking data collection. Additionally, the LiDAR sensor captures only three-dimensional data and is independent of illumination conditions, ensuring stable operation throughout the day while safeguarding privacy by not capturing imagery. These features align with the requirements of city agencies for parking data collection. The workflow follows a simple trend without the need for complex training, as typically seen in machine learning-based methods, and instead relies on parameter tuning based on real-world environmental factors. To validate the effectiveness of our method, we collected curbside parking data for five days at a midtown traffic junction with eight parking spaces. Manual validation confirmed a 95% match between identified parking events and observed data across different time periods. The study further presents parking statistics based on the identified events, revealing crucial insights about parking usage in the study area.