Method For Estimation Of Volume Research Articles

Two methods based on spline quasi-interpolants to estimate volumes enclosed by parametric surfaces

In this study, we explore two methods based on spline quasi−interpolants (abbr. QI) that rely exclusively on point evaluations to estimate the volume of a closed parametric surface. The first method involves a division of this surface evenly into parallel slices in order to approximate the contours of each slice using quadratic QI. Subsequently, we calculate numerically the areas of the interior surfaces, which will be used in a quadrature formula to obtain the approximate volume. The second method uses a bivariate QI to calculate the entire area, and an integration formula is applied to estimate the desired volume. In both methods, we study the associated error estimates. Numerical examples are provided to illustrate our methods, as well as a real-world application, as well as a study of the effect of noise on the robustness of both volume estimation methods.

A vision-based dietary survey and assessment system for college students in China

Understanding the current dietary habits of college students is essential due to the pressing issues of unbalanced diets and insufficient nutrition. However, traditional approaches frequently depend on recollection, which can introduce unconscious bias and make them difficult to implement. Herein, we introduce a new computer vision system to evaluate the dietary habits of college students in China. A specialized food dataset comprising RGB-D images, recipes with ingredient masses, and nutrient composition was created using data collected from college canteens. First, object detection models were utilized to identify food categories and locations. Subsequently, we introduced a method for automatically estimating the food volume of nonstandard portions using position and depth information. The final nutrients were derived directly or indirectly through the database. Experimental results demonstrate the applicability of the YOLOv8 object detection model and volume estimation method to our tasks. To support the development of devices for detecting food intake and diet management, we have made our dataset publicly available. The College Canteen Food-100 dataset is publicly available at https://github.com/zichengzichengzi/CC-FOOD-100.

Identification and Characterization of Reclaimed and Underclaimed Mine Features Using Lidar and Temporal Remote Sensing Methods within the Coastal Plain Uranium Mining Region of Texas

We developed a spatiotemporal mapping approach utilizing multiple techniques for distinguishing and mapping known reclaimed mine sites from “unreclaimed” mine sites in a historic uranium mining district along the South Texas Coastal Plains. Lidar laser scanning penetrates the vegetation canopy to expose anthropogenic modifications to the landscape. The Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features. Visual interpretation of Landsat imagery and time-series analysis augmented the Lidar analysis revealing the temporal life cycle of mining. The combination of bare earth texture with time-lapse and time-series analyses revealed areas of disturbance for reclaimed mines. The spatiotemporal mapping approach proved to be most useful in identifying and characterizing the known mine pit and pile features, reclamation status, and areas of disturbance due to mining. Two mine waste volume estimation methods resulted in a 21% difference indicating that although the approach helps to map mine features and areas of mining disturbance for the purposes of mine land inventory, additional information is needed to improve the estimation of buried mine waste at reclaimed mine sites.

Abstract P450: A dual probe ultrasound vector Doppler method for a robust and reliable estimation of blood flow volume and pulse wave velocity on the carotid artery

Background: The arterial pulse wave velocity (PWV) is a strong marker of cardiovascular risk and hypertension. The development of devices enabling a robust and reliable assessment of PWV could play an important role in the early diagnosis and monitoring of those diseases. In this work, we propose an ultrasound technique to estimate blood flow volume (BFV) and PWV by simultaneously using two probes. Methods: Two linear arrays, held by a handle, image two cross-sections of the artery, on two planes intersecting at 40° at the vessel center (A). B-mode and color flow Doppler (CFD) images are reconstructed at high frame rate (HFR): the cross-section HFR CFDs, providing vector-Doppler imaging, avoid any assumptions on flow velocity direction and profile, thus enabling reliable BFV estimates; the HFR B-mode allows tracking the arterial wall, thus enabling robust and time-resolved estimates of the lumen area (B). PWV is estimated as the ratio between the change in flow and cross-sectional area during the early systole (C). The method was preliminary verified by repeating 12 times the BFV and PWV estimation on the carotid artery of 8 healthy volunteers. Results: PWV and BFV measures were in the physiological range of 4.0-6.6 m/s (D) and 4.2-10.8 cm 3 /s, respectively. Measures were repeatable as proved by narrow interquartile ranges and low average standard deviations (1.4 m/s and 1.1 cm 3 /s, for PWV and BFV), which confirm the method's robustness. Conclusions: The proposed technique is suitable for BFV and PWV estimations. Since it is robust to probe repositioning and does not rely on any assumption of flow direction and profile, it could be implemented on wearable cardio-vascular wellness monitoring devices.

Advances in Deep Learning Methods for Prostate Segmentation and Volume Estimation in Ultrasound Imaging

Accurate prostate volume estimation is crucial for effective prostate disease management. Ultrasound (US) imaging, particularly transrectal ultrasound, offers a cost-effective and rapid assessment. However, US images often suffer from artifacts and poor contrast, making prostate volume estimation challenging. This review explores recent advancements in deep learning (DL) techniques for automatic prostate segmentation in US images as a primary step toward prostate volume estimation. We examine various DL architectures, including traditional U-Net modifications and innovative designs incorporating residual connections, multi-directional image data, and attention mechanisms. Additionally, we discuss pre-processing methods to enhance image quality, the integration of shape information, and strategies to improve the consistency and robustness of DL models. The effectiveness of these techniques is evaluated through metrics such as the Dice Similarity Coefficient, Jaccard Index, and Hausdorff Distance. The review highlights the potential of DL in improving prostate volume estimation accuracy and reducing clinical workload while also identifying areas for future research to enhance model performance and generalizability.

Automated eyeball volume measurement based on CT images using neural network-based segmentation and simple estimation

With the increase in the dependency on digital devices, the incidence of myopia, a precursor of various ocular diseases, has risen significantly. Because myopia and eyeball volume are related, myopia progression can be monitored through eyeball volume estimation. However, existing methods are limited because the eyeball shape is disregarded during estimation. We propose an automated eyeball volume estimation method from computed tomography images that incorporates prior knowledge of the actual eyeball shape. This study involves data preprocessing, image segmentation, and volume estimation steps, which include the truncated cone formula and integral equation. We obtained eyeball image masks using U-Net, HFCN, DeepLab v3 +, SegNet, and HardNet-MSEG. Data from 200 subjects were used for volume estimation, and manually extracted eyeball volumes were used for validation. U-Net outperformed among the segmentation models, and the proposed volume estimation method outperformed comparative methods on all evaluation metrics, with a correlation coefficient of 0.819, mean absolute error of 0.640, and mean squared error of 0.554. The proposed method surpasses existing methods, provides an accurate eyeball volume estimation for monitoring the progression of myopia, and could potentially aid in the diagnosis of ocular diseases. It could be extended to volume estimation of other ocular structures.

Comparing Automated Subcortical Volume Estimation Methods; Amygdala Volumes Estimated by FSL and FreeSurfer Have Poor Consistency.

Subcortical volumes are a promising source of biomarkers and features in biosignatures, and automated methods facilitate extracting them in large, phenotypically rich datasets. However, while extensive research has verified that the automated methods produce volumes that are similar to those generated by expert annotation, the consistency of methods with each other is understudied. Using data from the UK Biobank, we compare the estimates of subcortical volumes produced by two popular software suites: FSL and FreeSurfer. Although most subcortical volumes exhibit good to excellent consistency across the methods, the tools produce diverging estimates of amygdalar volume. Through simulation, we show that this poor consistency can lead to conflicting results, where one but not the other tool suggests statistical significance, or where both tools suggest a significant relationship but in opposite directions. Considering these issues, we discuss several ways in which care should be taken when reporting on relationships involving amygdalar volume.

Prediction of rain erosion damage progression using disdrometer rain data: The importance of liquid water content

Wind turbine blade erosion poses a significant challenge to the durability and performance of wind turbines. Modeling of rain erosion damage, considering atmospheric conditions, improves our understanding of the progression of leading-edge erosion on wind turbine blades. In this study, we investigate the impact of varying raindrop characteristics on rain erosion damage development. We analyse 2.5 years of data from a disdrometer, which measures the size and velocity of falling rain droplets, at Risø campus. Various post-processing methods of the disdrometer data are used for estimating representative droplet diameters and fall velocities for each rain event. We compare measured droplet fall velocities with theoretical terminal velocities, revealing a necessity for revising theoretical approaches to raindrop fall velocity for erosion damage modeling. The measured rain rates and representative fall velocities are used to calculate the liquid water content in the air. We introduce a bin-wise summation method for estimating the liquid water content, circumventing the need for representative droplet assumptions. As this method provides the most accurate input for the damage model, we benchmark the other post-processing methods against it and employ it to evaluate bias estimates of associated damage predictions. The largest bias (22%) in accumulated damage is found with an arithmetic mean droplet diameter approach and the smallest bias (-2%) with the median volume estimation method for damage model input. Furthermore, we demonstrate that, for a given rainfall volume, smaller droplets contribute to larger accumulated damage compared to larger droplets.

Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank.

Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank. A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1 mm isotropic resolution from CMR short- and long-axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate, and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC). Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R2 and more significant P-values), particularly for sex, age, and body mass index. AUCs for all logistic regressions were higher for deep learning volumes than standard volumes (P < 0.001 for all four chambers at ED and ES). Neural network reconstructions of whole heart volumes had significantly stronger associations with CVD and risk factors than standard volume estimation methods in an automatic processing pipeline.

Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis.

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures.

Field estimation of fallen deadwood volume under different management approaches in two European protected forested areas

Abstract Fallen deadwood is essential for biodiversity and nutrient cycling in forest ecosystems. In modern forest management, there is growing interest in developing accurate and efficient methods for field estimation of deadwood volume due to its many benefits (e.g. carbon storage, habitat creation, erosion control). The most common methods for deadwood inventories are fixed-area sampling (FAS) and line-intersect sampling (LIS) methods. While the estimations of deadwood volume by LIS generally show results comparable to FAS estimations, active management (e.g. production forestry clearcutting, logging, and thinning activities) can impair LIS accuracy by changing local deadwood patterns. Yet, the comparison of LIS and FAS methods has typically focused on production forests where deadwood is limited and deadwood volumes are comparably low. In this study, we assessed fallen deadwood volume in two large national parks—one being a more actively managed landscape (including, e.g., selective thinning for maintaining cultural–historical values and enhancing recreational opportunities) with overall lower levels of fallen deadwood, and the other having a strict non-intervention approach with higher levels of deadwood. No significant differences between average FAS and LIS estimations of deadwood volumes were detected. Additional experimentations using simulated data under varied stand conditions confirmed these results. Although line-intersect sampling showed a slight overestimation and some variability at the individual plot level, it remains an efficient, time-saving field sampling method providing comparable results to the more laborious fixed-area sampling. Line-intersect sampling may be especially suitable for rapid field inventories where relative changes in deadwood volume rather than absolute deadwood volumes are of large interest. Due to its practicality, flexibility, and relative accuracy, line-intersect sampling may gain wider use in natural resource management to inform national park managers, foresters, and ecologists.

Estimating Lung Volume Capacity from X-ray Images Using Deep Learning

Estimating lung volume capacity is crucial in clinical medicine, especially in disease diagnostics. However, the existing estimation methods are complex and expensive, which require experts to handle and consequently are more error-prone and time-consuming. Thus, developing an automatic measurement system without a human operator that is less prone to human error and, thus, more accurate has always been a prerequisite. The limitation of radiation dose and various medical conditions in technologies like computed tomography was also the primary concern in the past. Although qualitative prediction of lung volume may be a trivial task, designing clinically relevant and automated methods that effectively incorporate imaging data is a challenging problem. This paper proposes a novel multi-tasking-based automatic lung volume estimation method using deep learning that jointly learns segmentation and regression of volume estimation. The two networks, namely, segmentation and regression networks, are sequentially operated with some shared layers. The segmentation network segments the X-ray images, whose output is regressed by the regression network to determine the final lung volume. Besides, the dataset used in the proposed method is collected from three different secondary sources. The experimental results show that the proposed multi-tasking approach performs better than the individual networks. Further analysis of the multi-tasking approach with two different networks, namely, UNet and HRNet, shows that the network with HRNet performs better than the network with UNet with less volume estimation mean square error of 0.0010.

A-Mode Ultrasound Bladder Volume Estimation Algorithm Based on Wavelet Energy Ratio Adaptive Denoising.

Assessing bladder function is pivotal in urological health, with bladder volume a critical indicator. Traditional devices, hindered by high costs and cumbersome sizes, are being increasingly supplemented by portable alternatives; however, these alternatives often fall short in measurement accuracy. Addressing this gap, this study introduces a novel A-mode ultrasound-based bladder volume estimation algorithm optimized for portable devices, combining efficient, precise volume estimation with enhanced usability. Through the innovative application of a wavelet energy ratio adaptive denoising method, the algorithm significantly improves the signal-to-noise ratio, preserving critical signal details amidst device and environmental noise. Ultrasonic echoes were employed to acquire positional information on the anterior and posterior walls of the bladder at several points, with an ellipsoid fitted to these points using the least squares method for bladder volume estimation. Ultimately, a simulation experiment was conducted on an underwater porcine bladder. The experimental results indicate that the bladder volume estimation error of the algorithm is approximately 8.3%. This study offers a viable solution to enhance the accuracy and usability of portable devices for urological health monitoring, demonstrating significant potential for clinical application.

Development and Validation of a Method of Body Volume and Fat Mass Estimation Using Three-Dimensional Image Processing with a Mexican Sample.

Body composition assessment using instruments such as dual X-ray densitometry (DXA) can be complex and their use is often limited to research. This cross-sectional study aimed to develop and validate a densitometric method for fat mass (FM) estimation using 3D cameras. Using two such cameras, stereographic images, and a mesh reconstruction algorithm, 3D models were obtained. The FM estimations were compared using DXA as a reference. In total, 28 adults, with a mean BMI of 24.5 (±3.7) kg/m2 and mean FM (by DXA) of 19.6 (±5.8) kg, were enrolled. The intraclass correlation coefficient (ICC) for body volume (BV) was 0.98-0.99 (95% CI, 0.97-0.99) for intra-observer and 0.98 (95% CI, 0.96-0.99) for inter-observer reliability. The coefficient of variation for kinetic BV was 0.20 and the mean difference (bias) for BV (liter) between Bod Pod and Kinect was 0.16 (95% CI, -1.2 to 1.6), while the limits of agreement (LoA) were 7.1 to -7.5 L. The mean bias for FM (kg) between DXA and Kinect was -0.29 (95% CI, -2.7 to 2.1), and the LoA was 12.1 to -12.7 kg. The adjusted R2 obtained using an FM regression model was 0.86. The measurements of this 3D camera-based system aligned with the reference measurements, showing the system's feasibility as a simpler, more economical screening tool than current systems.

Uncertainties estimation in surveying measurands: application to volumes

Volume represents a measurand of great interest in civil engineering and construction works. The estimation of this measurand is a problem already solved by surveying engineering, but the quantification of its uncertainty has been overlooked. As a result, the inaccurate estimation of the volume can lead to significant deviations in the execution costs of earthworks. Moreover, it is not possible to comply with the internationally accepted requirements concerning the expression of measures with an indication of its uncertainty, i.e. the guidelines of the Bureau International des Poids et Mesures. In this context, this paper presents a methodology for the quantification of uncertainty in the surveying measurement of volumes, which is generally carried out through digital terrain models processed by computer-aided design or specific surveying software. Two methods for volume estimation are presented and the variance–covariance propagation laws are applied to each of them, leading to the computation of volume uncertainty from measures of position coordinates for which uncertainties are known. Then, the developed methods for uncertainty estimation are successfully tested in different scenarios. The conceptual and mathematical developments for the uncertainty quantification in the computation of volumes resulted in closed-form algorithms implemented in MATLAB that can be potentially incorporated into commercial surveying software.

Evaluation of superficial xenograft volume estimation by ultrasound and caliper against MRI in a longitudinal pre-clinical radiotherapeutic setting.

Accurate tumor volume estimation is important for evaluating the response to radionuclide therapy and external beam radiotherapy as well as to other pharmaceuticals. A common method for monitoring the growth of subcutaneous tumors in pre-clinical models and assessing the treatment response is to measure the tumor length and width by external calipers to estimate its volume. This procedure relies on an assumption of a spheroidal tumor shape wherein the tumor depth equals the width and can yield considerably inaccuracies. Ultrasound imaging is a non-invasive technique that can measure all three axes of the tumor and might be an alternative to caliper measurement with potentially greater accuracy and comparable ease-of-use and throughput. Both 2D and 3D ultrasound imaging are possible, the former offering short scan times without the need for anesthesia and heating-valuable factors for longitudinal studies in large animal cohorts. Nevertheless, tumor volume estimation accuracy by 2D ultrasound imaging has seen limited investigation. In this study we have evaluated the accuracy of tumor volume estimation by caliper and 2D ultrasound with comparisons to reference measurements by magnetic resonance imaging (MRI) in a pre-clinical model of prostate cancer treated with either external beam radiotherapy, radionuclide therapy, or no treatment. Tumor volumes were measured longitudinally in 29 mice by caliper, ultrasound, and MRI before and after external beam radiotherapy, [177Lu]Lu-PSMA-617 radionuclide therapy, or no treatment. Caliper measurements had a marked bias, overestimating the tumor volumes by a median of 150% compared to MRI. Ultrasound measurements were markedly more accurate, with a median bias of -21% compared to MRI. Ultrasound imaging is a reliable and accurate method for tumor volume estimation in pre-clinical models of radiotherapy, whereas caliper measurements are prone to overestimation.

Factors influencing the reliability of a CT angiography-based deep learning method for infarct volume estimation.

CT angiography (CTA)-based machine learning methods for infarct volume estimation have shown a tendency to overestimate infarct core and final infarct volumes (FIV). Our aim was to assess factors influencing the reliability of these methods. The effect of collateral circulation on the correlation between convolutional neural network (CNN) estimations and FIV was assessed based on the Miteff system and hypoperfusion intensity ratio (HIR) in 121 patients with anterior circulation acute ischaemic stroke using Pearson correlation coefficients and median volumes. Correlation was also assessed between successful and futile thrombectomies. The timing of individual CTAs in relation to CTP studies was analysed. The strength of correlation between CNN estimated volumes and FIV did not change significantly depending on collateral status as assessed with the Miteff system or HIR, being poor to moderate (r = 0.09-0.50). The strongest correlation was found in patients with futile thrombectomies (r = 0.61). Median CNN estimates showed a trend for overestimation compared to FIVs. CTA was acquired in the mid arterial phase in virtually all patients (120/121). This study showed no effect of collateral status on the reliability of the CNN and best correlation was found in patients with futile thrombectomies. CTA timing in the mid arterial phase in virtually all patients can explain infarct volume overestimation. CTA timing seems to be the most important factor influencing the reliability of current CTA-based machine learning methods, emphasizing the need for CTA protocol optimization for infarct core estimation.

A whale optimization detection (WOD) method for bladder volume estimation with bioelectrical impedance spectroscopy (BIS)

A whale optimization detection (WOD) method is presented for rapid bladder volume estimation with bioelectrical impedance spectroscopy (BIS). Firstly, the WOD method for bladder volume estimation is established, which includes a bladder volume estimation electrical equivalent circuit (EEC) model and a rapid fitting strategy. Secondly, an abdominal phantom is constructed, which consists of an abdominal cavity shell and four bladders with volumes of V b = 30 ml, 100 ml, 200 ml, and 300 ml. Next, BIS experiments are conducted using the phantom with the WOD method fitted to the obtained data. The results demonstrated a high linear correlation coefficient R 2 > 0.987 between the experimental and WOD results. Finally, in vivo testing is performed using both the Caresono HD-5 ultrasonic bladder scanner and the proposed WOD method. The EEC parameters obtained by the WOD method form a mathematical formula that accurately describes the subject’s bladder volume, with a detection correlation coefficient r = 98.71%, and the maximum relative error δ = 5.88% between WOD and ultrasound testing results. In conclusion, the proposed method is expected to be applied for rapid monitoring of patients’ bladder volume.

INDIVIDUAL TREE AGB ESTIMATION BASED ON FRACTAL PARAMETERS AND TREE VOLUME

Abstract. Forest is an important component of ecosystem. To estimate forest above-ground biomass (AGB) accurately, this paper proposed an individual tree AGB estimation method based on fractal geometry and individual tree volume. In this study, fractal parameters, such as fractal dimension and intercept were first calculated. And then, a fast tree volume estimation method based on point clouds voxelization was proposed. By combining fractal parameters, tree volume and specific wood density together, an individual tree AGB estimation method was developed. The datasets of three different tree species with harvest referenced AGB values were used for evaluating the performance of the developed model. Experimental results showed that the coefficient of determination (R2) of the developed model was 0.853. Compared with other four traditional allometric models, the proposed model performs the best no matter which accuracy indicator was adopted.

Automatic cochlear multimodal 3D image segmentation and analysis using atlas–model-based method

Objectives To propose an automated fast cochlear segmentation, length, and volume estimation method from clinical 3D multimodal images which has a potential role in the choice of cochlear implant type, surgery planning, and robotic surgeries. Methods: Two datasets from different countries were used. These datasets include 219 clinical 3D images of cochlea from 3 modalities: CT, CBCT, and MR. The datasets include different ages, genders, and types of cochlear implants. We propose an atlas–model-based method for cochlear segmentation and measurement based on high-resolution μCT model and A-value. The method was evaluated using 3D landmarks located by two experts. Results: The average error was 0.61 ± 0.22 mm and the average time required to process an image was 5.21 ± 0.93 seconds (P<0.001). The volume of the cochlea ranged from 73.96 mm 3 to 106.97 mm 3 , the cochlear length ranged from 36.69 to 45.91 mm at the lateral wall and from 29.12 to 39.05 mm at the organ of Corti. Discussion: We propose a method that produces nine different automated measurements of the cochlea: volume of scala tympani, volume of scala vestibuli, central lengths of the two scalae, the scala tympani lateral wall length, and the organ of Corti length in addition to three measurements related to A-value. Conclusion: This automatic cochlear image segmentation and analysis method can help clinician process multimodal cochlear images in approximately 5 seconds using a simple computer. The proposed method is publicly available for free download as an extension for 3D Slicer software.