Volume Estimation Research Articles (Page 1)

Fluid monitoring is critical for patients on maintenance hemodialysis. Bioimpedance enables estimation of fluid volumes from measures of electrical tissue properties. However, empirical equations are needed to approximate key variables, especially in wrist-to-ankle bioimpedance measurements, introducing potential errors. Here, we provide a technical overview of electrical impedance, derivation of fluid volumes from different bioimpedance methods and electrode setups, as well as sources of error including the assumption of constant resistivity, constant body temperature, and vendor-specific equations to derive fluid overload. We summarize the validity of bioimpedance methods in hemodialysis and conclude that irrespective of error sources reported above, segmental bioimpedance, where limbs and the trunk are measured separately, may be more accurate compared to the convenient wrist-to-ankle measurement. We argue that insufficient correction for variable body shape in wrist-to-ankle measurements jeopardizes this methodology, reporting here our analyses by means of theory and data simulation, where we found that conventional wrist-to-ankle bioimpedance underestimated extracellular fluid volume with increasing body fat percentage. The error could be reduced by using subject specific body shape correction based on high-resolution 3D models. Finally, we attempt to provide guidance for identifying and mitigating common issues of wrist-to-ankle bioimpedance. While more convenient than segmental measurements, wrist-to-ankle bioimpedance may underestimate fluid volumes in obesity when body shape is not properly accounted for. Novel techniques, including smartphone-based 3D scans of the body, could potentially facilitate individualizing body shape correction to improve fluid volume estimates.

Petroleum and natural gas are among the most critical energy sources in contemporary societies, still impossible to replace with recoverable resources. They are projected to play a pivotal role in addressing the global energy demands in the near future. Achieving energy security for the present turbulent times is of utmost importance. The discovery and development of new hydrocarbon deposits, along with increasing productivity from existing fields. The majority of onshore oil and gas fields in Bulgaria are in a mature to final stage of exploitation, thus emphasizing the need for innovative approaches and modern methods for outlining perspective exploration territories. Some of the economic oil fields are still in production (Tyulenovo, Dolni and Gorni Dabnik, Dolni Lukovit - Staroseltsi and Burdarski Geran) and their recoverable potential remains to be fully tapped. Conversely, a number of other fields have been classified as depleted or with minimal remaining reserves, which seriously raises the question of their future (e.g., Devetaki, Pisarovo, Aglen and Deventsi). These "depleted" deposits are of significant interest due to the possibilities to reassess and apply modern technologies for optimization and increasing the yield from already exhausted fields. Therefore, the primary goals are enhancing the recovery factor to prolong the operational lifespan of existing brownfields and reassessing the hydrocarbon perspective areas in Northern Bulgaria. Moreover, a significant set of geological, geophysical and technical data concerning hydrocarbon accumulations is available for reassessment. This extensive data base provides a robust foundation for contemporary characterization and evaluation of natural reservoirs in the case of Devetaki gas condensate field and overall evaluation of several perspective adjacent areas (Bohot, Gradina, Kriva Bara, Bazovets, Tarnak and etc.). It also facilitates quantitative estimations of resource and reserve volumes within these reservoirs as well as delineation of future exploration territories. The integration of software platforms with modern geoscience concepts offers a cost-effective tool for economic growth. This study highlights the need for realistic geological models and production plans to enhance recovery from mature oil and gas fields in Bulgaria. Reassessment of the promising areas where hydrocarbons are present will also provide a new in-depth view on the future oil and gas sustainable exploration.

In atrial fibrillation (AF), atrial biomechanics are altered, reducing atrial movement. It remains unclear whether these changes are due to altered anatomy, myocardial stiffness, or constraints from surrounding structures. Understanding the causes of changed atrial deformation in AF could enhance tissue characterization and inform AF diagnosis, stratification, and treatment. We created patient-specific anatomical models of the left atrium (LA) from CT images. Passive LA biomechanics were simulated using finite deformation continuum mechanics equations. LA stiffness was represented by the Guccione material law, where α scaled the anisotropic stiffness parameters. Regional passive stiffness parameters were calibrated to peak regional deformation during the reservoir phase and validated against deformation transients derived from retrospective gated CT images during the reservoir and conduit phase. Physiological LA deformation varies regionally, with the roof deforming significantly less than other regions during the reservoir phase. The fitted model matched peak patient deformations globally and regionally with an average error of [Formula: see text] mm over our cohort. We compared deformation transients through the reservoir and conduit phases and found that the simulated deformation transients were within an average of [Formula: see text] mm per unit time of the CT-derived deformation transients. Regional stiffness varied across the atria with average α values of 1.8, 1.6, 2.2, 1.6 and 2.1 across the cohort in the anterior, posterior, septum, lateral and roof regions respectively. Using mixed effect models, we found no correlation between regional patient LA deformation and regional estimates of wall thickness or regional volumes of epicardial adipose tissue. We found a significant correlation between regionally calibrated stiffness and CT-derived LA biomechanics (p = 0.023). We have shown that regional heterogeneity in stiffness contributes to regional LA biomechanics, while anatomical features appeared less important. These findings provide insight into the underlying causes of altered LA biomechanics in AF.

This study evaluates the use of drone - mounted LiDAR scanning to estimate the volume of post - flotation tailings accumulated in sedimentation ponds located in Bukowno, southern Poland. The survey was conducted using a DJI Matrice 350 RTK UAV equipped with th e Zenmuse L2 LiDAR sensor, supported by high - accuracy GNSS measurements using the Topcon HiPer XR receiver. The main goal was to generate a high - resolution 3D model of the tailings storage area and assess the geometric accuracy of horizontal and vertical g eodetic coordinates using control points and statistical validation. Post - processing of LiDAR data was performed using DJI Terra, followed by spatial and statistical analyses in QGIS and RStudio. Statistical evaluation included descriptiv e statistics, Tuke y’s outlier detection, and the Shapiro – Wilk test to verify the normality of error distributions. The horizontal accuracy was assessed using 10 control points, while the vertical accuracy relied on 46 measured control points. The analysis confirme d the high accuracy of the generated model, with average absolute elevation error not exceeding 1.7 cm and horizontal deviations ranging from 1.7 cm (Δx) to 3.6 cm (Δy). The volume of deposited tailings above the reference elevation of 320.00 m a.s.l. was calcul ated to be 37.49 million m³ over an area of 137.9 hectares. The Eastern pond was further divided into operational subsections to supp ort functional assessment and monitoring strategies .

Background: Respiratory exercises play a key role in rehabilitation programs, especially for older adults and individuals with chronic pulmonary conditions. Despite growing interest in wearable sensors for home-based care, structured reference metrics to quantitatively characterize respiratory exercises are still limited. This study aimed to provide a quantitative characterization of respiratory exercises and evaluate the level of agreement between a low-cost prototypical sensor and a commercial one. Methods: Eleven older adults (9 females; age = 72.6 ± 5.0 years; height = 1.66 ± 0.09 m; mass = 68 ± 10 kg) performed a structured respiratory exercises protocol. Algorithms were developed to identify respiratory cycles, their execution time, and parameters related to respiratory capacity, using accelerometer signals from the two wearable sensors placed on the rib cage. Results: The average respiratory cycle duration ranged from 2.8 to 4.3 s, with normalized inspiratory and expiratory peaks. Tidal volume variability was minimal, confirming consistency in breathing patterns across exercises. User comfort was high (mean VAS = 8.7). Sensor comparison confirmed strong agreement between the two sensors in detecting respiratory cycles, though some variability was observed in timing and tidal volume estimation. Conclusions: These findings suggest that even simple accelerometers can reliably capture key respiratory parameters, supporting the feasibility of using wearable sensors to monitor structured respiratory exercises performed in home-based settings.

Abstract. The measurement of ice thickness is of great importance for the accurate estimation of glacier volume and the delineation of bedrock topography. In particular, this is a crucial factor in forecasting the future evolution of glaciers in the context of a changing climate. In order to derive the ice thickness, the travel time of electromagnetic waves in radargrams acquired by radio-echo sounding (RES) systems is analyzed. This can only be achieved by identifying the ice surface and underlying ice bottom in corresponding radargrams. Manually identifying these two reflection horizons in RES data is a laborious and time-consuming process. Consequently, scientists are attempting to automate this task through the use of techniques such as deep learning. Such automation can significantly reduce the time between a field campaign and the calculation of the glacier's ice thickness distribution. In this paper, we present the first benchmark dataset for delineating the ice surface and bottom boundaries in RES data to facilitate standardized comparisons of deep learning models in the future. The “IceAnatomy” dataset comprises radargrams and the corresponding manual picks, amounting to a total of over 45 000 km of observations. The RES data originate from three sources: FAU (Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Geography), CReSIS (Center for Remote Sensing and Integrated Systems), and AWI (Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research). The dataset comprises different RES systems as well as different pre-processing methods. In addition, the data were acquired over a large range of geographical and glaciological settings, featuring different thermal regimes present in Antarctica and the Southern Patagonian Ice Field. This diversity ensures that the models' behaviors can be analyzed in different scenarios. We define a standardized train–test split for each source in the dataset. This allows us to introduce not only a baseline model trained on the entire training set (the “omni”-model), but also three source-specific baseline models. The source-specific models are trained exclusively on the subset of the training data acquired by the specified source. The baseline models provide an initial benchmark against which subsequent models can be compared. The source-specific models demonstrate more accurate results than the omni-model. For the FAU, CReSIS, and AWI test sets, the source-specific models achieve mean meter errors of 2.1, 23.1, and 4.9 m for the ice surface and 9.1, 78.2, and 29.3 m for the ice bottom. In relation to the mean measured ice thickness of the test set, these errors equate to 1.2 %, 3.1 %, and 0.3 % for the ice surface and 4.9 %, 10.4 %, and 1.5 % for the ice bottom. The dataset and implementation are available at https://doi.org/10.5281/zenodo.14036897 (Dreier et al., 2024) and https://doi.org/10.5281/zenodo.14038570 (Dreier, 2024).

A methodological framework is provided for characterizing large-scale forest resource distribution in South Korea, along with a baseline for sustainable forest management practices. This study aimed to establish a baseline framework that integrates satellite and ground-based data for nationwide growing stock volume (GSV) estimation. Several machine learning models were applied and compared for estimating GSV across South Korea using Sentinel-2 imagery, national forest inventory data, and topographic information. Four algorithms, namely, k-nearest neighbors (kNN), random forest (RF), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost), were evaluated. The ensemble methods outperformed kNN, with RF demonstrating the highest accuracy (coefficient of determination and root mean squared error of 0.56 and 66.9 m3/ha, respectively). Accuracy assessment shows that kNN performed relatively well near the mean GSV (≒200 m3/ha), but its accuracy decreased sharply toward the extremes, failing to represent plots above 400 m3/ha. Estimation accuracy also varied substantially with stand height, which was identified as the primary predictor, and kNN was the most affected. These findings suggest that the structural complexity and mountainous terrain of South Korean forests may amplify the limitations of distance-based methods, reinforcing the need for improved 3D structural predictors such as satellite-derived stand height.

Abstract Submarine landslides have the potential to be major geohazards as they can destroy seafloor infrastructure such as communication cables and cause tsunamis. The volume of material displaced during the landslide is one factor that determines its hazard and is typically estimated using bathymetric and/or seismic data. Here, we use various established methods to determine the initial failed volume based on a well-constrained case study, the Ana Slide, a small slope failure in the Eivissa Channel off the eastern Iberian Peninsula. We find that, not only, the availability and quality of marine-geophysical data, but also the emplacement mechanism affects how precisely the volume can be estimated. In general, the volume estimation based on comparison of recent and reconstructed pre-failure seafloor topographies yields conservative, yet robust estimates for the volume mobilized. In contrast, volumes estimated from seismic data may be overestimated if the nature of the chaotic, transparent, or disrupted seismic facies commonly used to identify landslide material is unknown.

Estimation of Flow Volume in Wadi Harran Basin in Eastern Iraq Using the SCS-CN Meth

7T MRI is a promising clinical technology for epilepsy imaging. Quantification of hippocampus volume on MRI is a clinically useful biomarker in epilepsy. Applying automated hippocampus volume measurement tools to 7T MRI is needed to optimize the use of clinical ultra-high-field strength epilepsy imaging. The objective of this study is a performance evaluation of automated hippocampal volume measurement software at 7T MRI in both normal participants and those with seizure disorders. 7T MRI examinations were prospectively acquired in 50 participants. A subset of participants also underwent 3T MRI examinations, and a subset underwent 2 separate 7T acquisitions. Automated segmentation of the hippocampus was performed with 2 commonly used software packages (FreeSurfer and NeuroQuant) at 3T and 7T, with hippocampal volumes calculated for segmentations without any visually unacceptable errors as determined by radiologist review. Hippocampal volumes were also measured from manual segmentations, and the intraclass correlation coefficient (ICC) was used to compare data with automated segmentation volumes. Visually unacceptable automated hippocampus segmentation errors occurred more frequently at 7T than at 3T with NeuroQuant (11.0% versus 7.14%) and FreeSurfer (12.5% versus 0%). Computerized volume measurements at 7T correlated poorly with manual segmentation for both software programs (ICC <0.4). Hippocampal volume estimate correlation between matched 7T and 3T MRI in the same participant was fair (ICC = 0.4-0.59) to good (0.6-0.75) for software and manual segmentation. For repeated 7T MRI examinations in the same participant, hippocampus segmentation reproducibility was excellent (0.75) for automated software but poor (< 0.4) for manual segmentation. Computerized volume measurement of the hippocampus at 7T correlates poorly with volumes obtained through manual segmentation and suboptimally with matched 3T examination measurements, but is highly reproducible at 7T within the same participant. Segmentation errors are more common with 7T examinations, and further development of a hippocampal segmentation method specific to 7T MRI is needed to fully realize the benefits of 7T MRI for imaging patients with epilepsy.

Non-invasive tidal volume estimation with wearable sensors using a high-gain observer and deep learning.

Single-image estimation of tree volume via pixel-mapped 3D reconstruction: A low-cost solution using deep learning and curvature segmentation.

Region-wise stacking ensembles for estimating brain-age using structural MRI.

Abstract Volcanic eruptions cause large‐scale topographic changes, through the emplacement of lava flows and lava domes, the formation of craters and calderas, and thick ash and pyroclastic deposits. Here we analyze the TanDEM‐X Digital Change Map (DCM), which compares the DEM produced during 2010–2015 with satellite acquisitions collected in 2016–2022. The DCM covers 159 eruptions at 103 volcanoes; the data was good quality at 44 of these but not useable at 28. Topographic changes associated with volcanic activity was visible at 58 volcanoes including lava flows, domes, intrusions, pyroclastic flows, lahars, tephra fall, crater formation and landslides. We analyze five case studies in detail: Sierra Negra, Galápagos; Erta Ale, Ethiopia; Sangay, Ecuador; Ebeko, Russia; and Nabro, Eritrea. Our measurements of the lava flows at Sierra Negra and Nabro and crater formation at Ebeko agree to within 15% of previous measurements, confirming the accuracy of the TanDEM‐X DCM in volcanic areas. At Erta Ale, we find maximum lava thickness of &gt;40 m, greatly exceeding previous field‐based estimates (&lt;2.5 m); consequently, our total volume estimate is an order of magnitude higher. At Sangay, the patterns of height change are consistent with local reports, but our measurements have high uncertainties due to the prevalence of vegetative noise and steep topography. Overall, we demonstrate that the TanDEM‐X DCM can measure topographic changes at volcanoes, and in many cases allows us to make new measurements. Finally, we discuss the lessons learned from the TanDEM‐X DCM for planning future satellite missions, including the upcoming European Space Agency Harmony Mission.

Effect of metoclopramide on gastric volume and nausea and vomiting in fasted patients undergoing elective cesarean delivery: a randomized clinical equivalence trial.

Purpose. Development and application of a methodology for predicting the occurrence of a coal seam using numerical interpolation methods and three-dimensional geoinformation modeling. Methodology. The study is based on geological exploration data of the c 42 coal seam at the “Samarska” mine. Numerical interpolation methods and three-dimensional modeling in AutoCAD 3D were used. Based on geological data, a digital terrain model of the seam’s floor was constructed, and its thickness and variations across the area were determined. Using mathematical methods, an analysis of thinning and thickening zones of the seam was carried out, allowing for the estimation of coal extraction volumes, waste rock yield, and the operational ash content of coal. Findings. As a result of the study, a three-dimensional model of the c 42 coal seam of the “Samarska” mine was developed and its geological thickness was determined. Areas of thinning and thickening of the seam were identified, which made it possible to optimize the location of extraction pillars and preparatory workings. Volumes of coal to be mined and waste rock to be cut were calculated. The estimated operational ash content of coal was determined to be 34.4 %, which is an important factor for controlling the quality of the extracted product. The data obtained made it possible to optimize the parameters of cleaning operations, adapting the technological process to the geological conditions of the coal seam. Originality. The article proposes an improved approach to predicting coal seam occurrence using numerical interpolation and three-dimensional modeling, adapted to conditions with limited geological data. For the first time, a step-by-step construction with dynamic uprating of seam geometry is implemented, enhancing the accuracy of reserve estimation and the efficiency of mining design. Practical value. The developed methodology makes it possible to minimize exploration costs, improve the reserve estimation accuracy, reduce risks, and optimize mining operations. The results can be used to design production technology for other mines in Western Donbas, contributing to increased mining efficiency.

This study introduces a novel computer vision approach to automate documentation of anesthetic injection events in the operating room. The objective is to enhance documentation accuracy and reliability by providing precise identification of injection events and anesthetic amounts administered, while addressing stopcock placement variability. We developed a computer vision pipeline tailored for automated anesthetic injection documentation in surgical environments. The pipeline leverages the Segment Anything Model (SAM) for robust syringe segmentation, combined with vector similarity matching for generalization across different syringe sizes and occlusions. This few-shot segmentation strategy ensures generalization while minimizing annotation effort. The pipeline also integrates lightweight methods for motion detection, syringe classification, and volume estimation to ensure quasi-real-time performance. The system was tested on 304 injection events performed by 19 anesthesiologists using syringes of four sizes (3, 5, 10 and 20 ml). The pipeline achieved 100% injection-event detection sensitivity and an overall 86.3% documentation success rate. Volume estimation accuracy varied across syringe sizes, with mean absolute error (MAE) values of 0.10, 0.22, 0.37, and 0.61 ml for 3, 5, 10, and 20 ml syringes, respectively. Results compare favorably to manual measurements, which can have mean percentage errors of 1.4%–18.6%. Runtime optimization ensured quasi-real-time operation, processing each event within 10–12 s, supporting clinical workflow integration. This work presents a solution to significantly improve anesthetic injection documentation while enhancing patient safety, standardizing procedures, and reducing anesthesiologists’ workload, representing a fully automated, camera-only pipeline validated on clinicians in quasi-real-time.

Abstract. To promote sustainable forest management planning including biodiversity monitoring and to enable accurate estimates of stem volume, above-ground biomass, and carbon stocks, tree identification is essential to contemporary forest inventory. Deep learning models are now crucial tools for automating tree recognition over large, forested regions due to the growing availability of high-resolution LiDAR data. In order to identify individual trees using LiDAR-derived RGB raster imagery, this work compares two cutting-edge object identification architectures: YOLOv8 and YOLOv11. A total of 82 annotated images were utilized, rasterized at a resolution of 5 cm, and processed using two input resolutions (640×640 pixel and 960×960 pixel), several model configurations (s, m, l, x), and augmentation settings (rotation and horizontal flip). To provide fair comparison, every model was trained and evaluated using the same methodology. Precision, recall, mAP50, and mAP50-95, standard detection metrics, were used to evaluate performance. The results show that YOLOv8 consistently beat YOLOv11 on all metrics, especially in its large and extra-large forms at high resolution. YOLOv8x with 960 pixel resolution and augmentation was the best-performing setup, with 0.974 precision, 0.837 recall, 0.934 mAP50, and 0.821 mAP50-95. The results demonstrate notable improvements in detection accuracy when compared to previous methods that used YOLOv4 or domain-specific structures like YOLOTree. With the use of rasterized UAV laser scanning data, our results highlight the potential of the YOLO architecture as a robust and scalable tool for automated, high-precision forest inventory.

The semiarid region of Brazil is among the most affected by water scarcity, primarily due to high evapotranspiration rates and recurrent drought events. This study aimed to evaluate the behavior of the water volume in the Epitácio Pessoa Reservoir under future climate scenarios, land use and land cover (LULC) changes, and projected population growth between 2030 and 2060. The methodological framework comprised the following steps: (a) estimation of future water demand for human consumption, (b) analysis of historical LULC changes, (c) prediction of LULC using a multilayer perceptron (MLP) neural network model, (d) calibration and validation of the SWAT hydrological model, (e) analysis of temperature and precipitation variability, (f) simulation of reservoir inflows using the calibrated SWAT model, and (g) estimation of future reservoir water volume. The results indicate an expansion of agricultural and pasture areas and a substantial reduction in open shrubby Caatinga by 2060. Climate projections suggest more severe droughts during the driest months, particularly from September to November. Under the high-emissions scenario (SSP5), all models forecast increased temperatures, with maximum values reaching approximately 34 °C, and December identified as the hottest month. The GFDL-ESM4 model consistently projected the highest maximum temperatures across most months. Hydrological modeling demonstrated satisfactory performance, with R2 and Nash–Sutcliffe efficiency values exceeding 0.5, particularly during calibration and validation phases. The results also identify critical periods between 2042 and 2049, during which reservoir levels may fall below the dead storage volume. Among all scenarios, SSP2 was the most critical in the medium and long term, with reservoir volumes reaching minimum levels more frequently and for longer durations, especially after 2040.

In ruminants, water ingestion causes an immediate and measurable decline in rumen temperature. While this physiological response is well established, its application for estimating water intake has not previously been investigated. This study assessed the suitability of a thermodynamic modelling approach to predict water intake in sheep using intra-ruminal temperature data. A fluid calorimetry equation was first validated under controlled laboratory conditions, then applied to data from six sheep fitted with intra-ruminal temperature loggers. Animals were offered water of known volume and temperature, and intake predictions were calculated using the temperature drop, baseline rumen temperature, water temperature, and estimated rumen volume based on liveweight. To improve volume estimation, additional rumen volume and liveweight data from a separate group were used to generate a generalised prediction equation for rumen volume. The approach underestimated the volume of water consumed, and so we developed a correction factor to account for physiological variation in the effective rumen volume at the time of drinking. Using the effective rumen volume in calculations resulted in predicted intake volumes that generally aligned with the measured values (P < 0.01). Our results demonstrate that measured changes in intra-ruminal temperature can be used to estimate water intake, offering a promising tool for precision livestock monitoring in extensive grazing systems.