Accuracy Of Volume Measurements Research Articles

Pediatric blood cultures-turning up the volume: a before and after intervention study.

The major determinant of blood culture (BC) diagnostic performance is blood volume, and pediatric sample volumes are frequently low. We aimed to assess BC volumes in our institution, design an intervention to increase volumes, and assess its impact. All pediatric BCs submitted over a 7-month period to the microbiology laboratory in a university hospital (including emergency department, pediatric ward, and neonatal unit) were included. A pre-intervention period assessed current practice. A multi-faceted intervention (education, guideline introduction, active feedback strategies) was collaboratively designed by all stakeholders. Impact was assessed in a post-intervention period. The main outcome measures included the percentage of samples adequately filled using three measures of sample adequacy (1) manufacturer-recommended minimum validated volume-> 0.5ml, (2) manufacturer-recommended optimal minimum volume-> 1.0ml, (3) newly introduced age-specific recommendations. Three hundred ninety-eight pre-intervention and 388 post-intervention samples were included. Initial volumes were low but increased significantly post-intervention (median 0.77ml vs. 1.52ml), with multivariable regression analysis estimating volumes increased 89% post-intervention. There were significant increases in all measures of volume adequacy, including an increase in age-appropriate filling (20.4-53.1%), with less improvement in those aged > 3 years. Overall, 68.4% of pathogens were from adequately filled cultures, while 76% of contaminants were from inadequately filled cultures. A pathogen was detected in a higher proportion of adequately filled than inadequately filled cultures (9.4% vs. 2.2%, p < 0.001). Conclusion: Blood volume impacts BC sensitivity, with lower volumes yielding fewer pathogens and more contaminants. Focused intervention can significantly improve volumes to improve diagnostic performance. What is Known: • Blood volume is the major determinant of blood culture positivity, and yet pediatric blood culture volumes are frequently low, resulting in missed pathogens and increased contamination. What is New: • Adequately filled (for age) blood cultures have a pathogen detection rate three times higher than inadequately filled blood cultures. • This interventional study shows that collaboratively designed multi-modal interventionsincluding focus on accurate volume measurement can lead to significant increases in blood volumes and improve blood culture diagnostic performance.

Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies.

In this case study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited datasets, undocumented modifications to machine settings, and sensor failures, this task renders a challenging application area for machine learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues such as sensor usability, data quality, and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management.

A study of membrane correction accounting for both curvature and tension in DEM simulations of triaxial tests of sand and ballast with two alternative flexible membrane models

In DEM simulations of triaxial tests, modelling a flexible lateral membrane is crucial and challenging. It is essential for the correct application of a uniform lateral pressure and for an accurate measurement of sample volume. Here, we introduce a membrane made of triangular facets, and model it as a continuum; we then compare this approach with a well-established method that uses a layer of bonded spheres. With either method, it is also possible to assess the additional stress applied by the membrane as it deforms, i.e. the difference between the stress applied at the boundary and the actual stress within the sample. It is shown that this difference has two origins: the tension developed in the membrane, as it deforms; and the curvature of the membrane, since this causes a vertical component of the confining pressure which can be significant. These findings may be used to inform and improve the membrane correction commonly used in experiments, where similar effects occur.Graphic abstract

Techniques for Measuring the Fluctuation of Residual Lower Limb Volume in Clinical Practices: A Systematic Review of the Past Four Decades

Increased pressure and shear stress distributions at the limb–socket interface are hypothesised to result from changes in the residual limb shape and volume, which can cause socket fit difficulties. Accurate residual limb volume measurements may aid clinicians in developing strategies to accommodate volume fluctuations. This review primarily aims to analyse the techniques available for measuring the residual lower limb volume that may be used in clinical settings, as documented in the works published over the previous four decades. A comprehensive search of articles in PubMed, ScienceDirect, Web of Science, and Google Scholar identified 904 articles, and further analysis resulted in only 39 articles being chosen to be analysed. Based on the findings, there are nine techniques available to measure the residual limb volume: water displacement, anthropometric measurement, contact probes, optical scanning, spiral X-ray computed tomography (SXCT), magnetic resonance imaging (MRI), ultrasound, laser scanning, and bioimpedance. Considering the variety of techniques for determining residual limb volume, it is critical to choose the ones that best suit clinicians’ objectives, and each technique has potential sources of error that should be avoided by taking precautionary action. A comprehensive study of the measurement techniques is needed since researchers have developed and extensively utilised many new measuring devices, especially handheld 3D laser scanners.

Preoperative and Intraoperative Scrotal Duplex Ultrasound in the Assessment of Varicocele: A Comparative Study

Background: This study aimed to compare the accuracy of preoperative and intraoperative scrotal duplex ultrasound (SDU) in assessing varicocele, a common cause of male subfertility.Methods and Results: The study was conducted on 20 male patients scheduled for subinguinal varicocelectomy at a tertiary care hospital from March 2022 to March 2023. We used Grey-Scale US and Doppler US to evaluate testicular size, vein diameter, arterial flow parameters, and retrograde flow. We found that the preoperative scrotal duplex was a good and accurate method for assessing the grading of varicocele, and there was no significant difference in vein diameter or venous reflux between preoperative and intraoperative assessments. However, we observed a significant increase in the number of veins and testicular volume during intraoperative SDU compared to preoperative scrotal SDU.Conclusion: SDU is a reliable tool for determining testicular volume and provides objective, accurate, and reproducible measurements of testicular volume.

3D modeling and volume measurement of bulk grains stored in large warehouses using bi-temporal multi-site terrestrial laser scanning data

Terrestrial laser scanning (TLS) is a promising technology for quantity checking huge grain stocks with low cost, light workload and high efficiency. Existing applications of TLS in bulk grain measurement and quantification lack the ability to capture complete structural information of grain bulks and thus will result in large errors. In this paper, we propose a bi-temporal TLS scheme for fast 3D modeling and accurate volume measurement of bulk grains stored in large warehouses. The scheme uses bi-temporal multi-site TLS datasets acquired under both empty and full or high loading conditions to obtain complete surface information about grain bulk’s structure. In order for a grain bulk’s all external surfaces and the 3D volumetric model reconstructed therefrom to be automatically derived from the bi-temporal TLS dataset, several dedicated methods are developed for the scheme. A fully automated marker-free strategy exploring structurally semantic information inherent in the large grain storehouses is adopted to register multi-scan TLS point cloud data captured in large-scale granary scenes. Also, a local minima-based region growing technique is devised to extract underlying surfaces from a granary scene point cloud model. Experiments show that the proposed 3D modeling and volume measurement scheme can work effectively and quickly in TLS-based granary field applications and repeated test data demonstrate its correctness, repeatability and accuracy. Compared with the conventional manual measurement approach, the bi-temporal TLS scheme can not only achieve much higher measurement precision, but also greatly improve efficiency by significantly reducing cost, workload, and manpower. It has good potential for use in the area of nation-wide grain inventory inspection in China.

Improved subcutaneous edema segmentation on abdominal CT using a generated adipose tissue density prior.

Edema, or swelling, is a common symptom of kidney, heart, and liver disease. Volumetric edema measurement is potentially clinically useful. Edema can occur in various tissues. This work focuses on segmentation and volume measurement of one common site, subcutaneous adipose tissue. The density distributions of edema and subcutaneous adipose tissue are represented as a two-class Gaussian mixture model (GMM). In previous work, edema regions were segmented by selecting voxels with density values within the edema density distribution. This work improves upon the prior work by generating an adipose tissue mask without edema through a conditional generative adversarial network. The density distribution of the generated mask was imported into a Chan-Vese level set framework. Edema and subcutaneous adipose tissue are separated by iteratively updating their respective density distributions. Validation results on 25 patients with edema showed that the segmentation accuracy significantly improved. Compared to GMM, the average Dice Similarity Coefficient increased from 56.0 to 61.7% ([Formula: see text]) and the relative volume difference decreased from 36.5 to 30.2% ([Formula: see text]). The generated adipose tissue density prior improved edema segmentation accuracy. Accurate edema volume measurement may prove clinically useful.

Accuracy of formula-based volume and image segmentation-based volume in calculation of preoperative cystic jaw lesions’ volume

ObjectiveThe aim of this study was to assess the accuracy of formula-based volume measurements and the 3D volume analysis with different software packages in the calculation of preoperative cystic jaw lesions’ volume. The secondary aim was to assess the reliability and the accuracy of 3 imaging software programs for measuring the cystic jaw lesions' volume in CBCT images.Materials and methodsThis study consisted of two parts: an in vitro part using 2 dry human mandibles that were used to create simulated osteolytic lesions to assess the accuracy of the volumetric analysis and formula-based volume. As a gold standard, the volume of each bone defect was determined by taking an impression using rapid soft silicone (Vinylight) and then quantifying the volume of the replica. Afterward, each tooth socket was scanned using a high-resolution CBCT. A retrospective part using archived CBCT radiographs that were taken from the database of the outpatient clinic of the oral and maxillofacial radiology department, Faculty of Dentistry, Minia University to assess the reliability of the 3 software packages. The volumetric data set was exported for volume quantification using the 3 software packages (MIMICS-OnDemand and InVesalius software). Also, the three greatest orthogonal diameters of the lesions were calculated, and the volume was assessed using the ellipsoid formula. Dunn’s test was used for pair-wise comparisons when Friedman’s test was significant. The inter-examiner agreement was assessed using Cronbach’s alpha reliability coefficient and intra-class correlation coefficient.ResultsRegarding the results of the retrospective part, there was a statistically significant difference between volumetric measurements by equation and different software (P value < 0.001, Effect size = 0.513). The inter-observer reliability of the measurements of the cystic lesions using the different software packages was very good. The highest inter-examiner agreement for volume measurement was found with InVesalius (Cronbach’s alpha = 0.992). On the other hand, there was a statistically significant difference between dry mandible volumetric measurements and Gold Standard. All software showed statistically significantly lower dry mandible volumetric measurements than the gold standard.ConclusionComputer-aided assessment of cystic lesion volume using InVesalius, OnDemand, and MIMICS is a readily available, easy to use, non-invasive option. It confers an advantage over formula-based volume as it gives the exact morphology of the lesion so that potential problems can be detected before surgery. Volume analysis with InVesalius software was accurate in determining the volume of simulated periapical defects in a human cadaver mandible as compared to true volume. InVesalius software proved that open-source software can be robust yet user-friendly with the advantage of minimal cost to use.

Comparison of Multiple Segmentation Methods for Volumetric Delineation of Primary Prostate Cancer with Prostate-Specific Membrane Antigen-Targeted 18F-DCFPyL PET/CT.

This study aimed to assess the accuracy of intraprostatic tumor volume measurements on prostate-specific membrane antigen-targeted 18F-DCFPyL PET/CT made with various segmentation methods. An accurate understanding of tumor volumes versus segmentation techniques is critical for therapy planning, such as radiation dose volume determination and response assessment. Methods: Twenty-five men with clinically localized, high-risk prostate cancer were imaged with 18F-DCFPyL PET/CT before radical prostatectomy. The tumor volumes and tumor-to-prostate ratios (TPRs) of dominant intraprostatic foci of uptake were determined using semiautomatic segmentation (applying SUVmax percentage [SUV%] thresholds of SUV30%-SUV70%), adaptive segmentation (using adaptive segmentation percentage [A%] thresholds of A30%-A70%), and manual contouring. The histopathologic tumor volume (TV-Histo) served as the reference standard. The significance of differences between TV-Histo and PET-based tumor volume were assessed using the paired-sample Wilcoxon signed-rank test. The Spearman correlation coefficient was used to establish the strength of the association between TV-Histo and PET-derived tumor volume. Results: Median TV-Histo was 2.03 cm3 (interquartile ratio [IQR], 1.16-3.36 cm3), and median TPR was 10.16%. The adaptive method with an A40% threshold most closely determined the tumor volume, with a median difference of +0.19 (IQR, -0.71 to +2.01) and a median relative difference of +7.6%. The paired-sample Wilcoxon test showed no significant difference in PET-derived tumor volume and TV-Histo using A40%, A50%, SUV40%, and SUV50% threshold segmentation algorithms (P > 0.05). For both threshold-based segmentation methods, use of higher thresholds (e.g., SUV60% or SUV70% and A50%-A70%) resulted in underestimation of tumor volumes, and use of lower thresholds (e.g., SUV30% or SUV40% and A30%) resulted in overestimation of tumor volumes relative to TV-Histo and TPR. Manual segmentation overestimated the tumor volume, with a median difference of +2.49 (IQR, 0.42-4.11) and a median relative difference of +130%. Conclusion: Segmentation of intraprostatic tumor volume and TPR with an adaptive segmentation approach most closely approximates TV-Histo. This information might be used to guide the primary treatment of men with clinically localized, high-risk prostate cancer.

Evaluation of a Fully Automated Method for Ventricular Volume Segmentation Before and After Shunt Surgery in Idiopathic Normal Pressure Hydrocephalus

Determination of the ventricle size in idiopathic normal pressure hydrocephalus (iNPH) is essential for diagnosis and follow-up of shunt results. Fully automated segmentation methods are anticipated to optimize the accuracy and time efficiency of ventricular volume measurements. We evaluated the accuracy of preoperative and postoperative ventricular volume measurements in iNPH by a magnetic resonance imaging (MRI)-based licensed software for fully automated quantitative assessment. Forty-eight patients diagnosed with iNPH were retrospectively analyzed. All patients received a ventriculoperitoneal shunt and had symptom grading and routine MRI preoperatively and 3-6 months postoperatively. Ventricular volumes, generated by fully automated T1-weighted imaging volume sequence segmentation, were compared with semiautomatic measurements and routine radiologic reports. The relation of postoperative ventricular size change to clinical response was evaluated. Fully automated segmentation was achieved in 95% of the MRIs, but showed various rates of 8 minor segmentation errors. The correlation between both segmentation methods was very strong (r >0.9) and the agreement very good using Bland-Altman analyses. The ventricular volumes differed significantly between semiautomated and fully automated segmentations and between preoperative and postoperative MRI. The fully automated method systematically overestimated the ventricles by a median 15 mL preoperatively and 14 mL postoperatively; hence, the magnitudes of volume changes were equivalent. Routine radiologic reports of ventricular size changes were inaccurate in 51% and lacked association with treatment response. Objectively measured ventricular volume changes correlated moderately with postoperative clinical improvement. A fully automated volumetric method permits reliable evaluation of preoperative ventriculomegaly and postoperative ventricular volume change in idiopathic normal pressure hydrocephalus.

A clinical study of the effect of 3D reconstruction on exophthalmos after an operation on an old orbital wall fracture.

Orbital blowout fracture is common in ocular trauma. Accurate measurement of orbital volume after fracture is key in improving intraocular correction. This study aims to explore the impact of 3D reconstruction technology in restoring normal exophthalmos in patients with old orbital wall fractures. A total of 31 patients were randomly divided into an experimental group (n= 15) and a control group (n= 16). For orbital wall repair and reconstruction, the conventional group used the conventional surgical scheme, and the 3D group used 3D printing technology. There was no statistical difference between the preoperative mean extraocular muscle volume of the healthy eye and the affected eye. However, the mean orbital volume (24.76 vs 27.11, P= 0.005) and mean retrobulbar fat volume (17.53 vs 16.42, P= 0.006) were significantly different between the healthy eye and the affected eye. After an average follow-up of 16 weeks, the differences in pre- and post-surgery exophthalmos in the two groups were 0.42 ± 0.08 mm and 1.63 ± 0.51 mm, respectively. The difference between the two groups was statistically significant (t= 4.42, P= 0.003). The complications were not statistically different. Using 3D reconstruction technology preoperatively can significantly improve exophthalmos in patients with old orbital wall fractures.

Design and experiment of a binocular vision-based canopy volume extraction system for precision pesticide application by UAVs

When unmanned aerial vehicles (UAVs) are used for orchard chemicals application, accurate measurement of the canopy volume can provide decision support for determining pesticide dosages, flight parameters, and droplet sizes. Using binocular camera ranging, this study presents a novel canopy segmentation algorithm that preprocesses light detection ranging data to extract sub-grid canopy volumes. A binocular vision-based canopy volume extraction system for UAV chemical application was developed. The system utilizes multi-degree-of-freedom adaptive balance technology to ensure that the binocular camera can still vertically detect the canopy even when the flight attitude changes. Performance experiments were conducted using artificial fruit trees with different leaf densities and regular cardboard box as measurement targets. The canopy volume measurements indicate that the new model accurately detects target contours. When flying at 2 m/s, the maximum errors between system-measured and actual volumes were 6.58 and 9.37 % for the rectangular and triangular, respectively. Increasing speeds and attitudes lead to increased errors and measurement variations. However, the position of the system relative to the target does not cause significant differences in results. The maximum measurement errors between system-measured and actual LiDAR values were 6.44 and 9.17 % for high- and low-density canopies, respectively. These results demonstrate that the proposed system has high measurement accuracy and provides a reliable precision UAV pesticide-spraying control system for plant protection based on real-time canopy detection.

Three-dimensional Echocardiography for the Early Detection of Cardiac Dysfunction in Patients with Rheumatoid Arthritis

Abstract Background Rheumatoid arthritis (RA) is a chronic, systemic inflammatory disease. Left atrial (LA) dysfunction is strongly linked to cardiovascular diseases, and the early detection of LA function in patients with RA is vital. Real-time three-dimensional echocardiography (RT-3DE) offers a noninvasive method to assess the structure and function of the LA. Aim of the study To assess LA volumes and phasic function in patients with RA using 3D transthoracic echocardiography. Materials and Methods This prospective case-control study included 162 subjects classified into two groups: Group 1 included 82 patients with RA, subdivided into an active RA group (n = 40) and an inactive RA group (n = 42), and Group 2 included 80 healthy matched controls. All study participants were examined using 2D and 3D transthoracic echo-cardiography. Results Diastolic dysfunction was significantly greater in patients with moderate and severe disease activity than in patients with mild disease activity. RT-3DE analysis of RA groups showed significantly higher maximum, minimum, and pre-atrial LA volumes compared to controls (p &lt;0.01), and these volumes were significantly higher in the active RA group than in the inactive RA group (p &lt;0.01). We also found significantly lower LA passive ejection fraction (EF) and active EF in both RA groups than the control group (p &lt;0.01), and significantly lower total EF in the active RA group than the inactive RA group (p &lt;0.01). Conclusion Patients with RA had increased 3D LA volumes and impaired mechanical function, especially in active RA. RT-3DE provides an accurate measurement of LA volumes and function, being a feasible and reproducible method in clinical applications.

A dual-parameter measuring method for wet steam based on the characteristics of differential pressure under phase-isolation state

To achieve accurate measurement of steam injection volume in the process of steam-driving heavy oil, a low-cost online metering device for wet steam flows is urgently needed. Primarily, the traditional Venturi device has a relative error due to the wet steam flow patterns. Therefore, a new physical model for wet steam measurement composed of a swirler and a Venturi was designed, based on the phase-isolation technology for multi-phase flow measurement. A theoretical explanation of the differential pressures was then proposed. Numerical simulations were conducted to investigate wet steam measurement, specifically under the condition of 10∼20 MPa. A new correlation for the steam quality and mass flow rate with the pressure drop was developed by the multiphase flow model via Eulerian. A novel wet steam dual-differential pressure measurement model was established by considering the effects of temperature and pressure, which predicts the wet steam overall mass flow rate and steam quality of the measurement device. Block P601 and Block P6 of the oilfield located in eastern China were selected as the development test area, and the differential pressure prediction was carried out for 68 different working conditions of two Wells based on the dual-parameter measurement model, which was then calibrated. The experimental fieldwork on Block P601 demonstrated that the feasibility of the dual-parameter measuring method was verified, with relative deviations of ±7% between the predicted and experimental values of differential pressure. After calibration of the measurement model, the predicted values were compared with the experimental values, yielding relative deviations of ±3.5% and ±5% for mass flow rate and steam mass measured online for wet steam, with Mean Absolute Percentage Errors (MAPE) of 1.73% and 2.21%. Thus, the dual-parameter measuring method has a certain engineering significance for the steam injection technology of heavy oil in oilfields.

Three-dimensional ultrasound to investigate synovitis in first carpometacarpal osteoarthritis: A feasibility study.

Synovitis is one of the defining characteristics of osteoarthritis (OA) in the carpometacarpal (CMC1) joint of the thumb. Quantitative characterization of synovial volume is important for furthering our understanding of CMC1 OA disease progression, treatment response, and monitoring strategies. In previous studies, three-dimensional ultrasound (3-D US) has demonstrated the feasibility of being a point-of-care system for monitoring knee OA. However, 3-D US has not been tested on the smaller joints of the hand, which presents unique physiological and imaging challenges. To develop and validate a novel application of 3-D US to monitor soft-tissue characteristics of OA in a CMC1 OA patient population compared to the current gold standard, magnetic resonance imaging (MRI). A motorized submerged transducer moving assembly was designed for this device specifically for imaging the joints of the hands and wrist. The device used a linear 3-D scanning approach, where a 14L5 2-D transducer was translated over the region of interest. Two imaging phantoms were used to test the linear and volumetric measurement accuracy of the 3-D US device. To evaluate the accuracy of the reconstructed 3-D US geometry, a multilayer monofilament string-grid phantom (10mm square grid) was scanned. To validate the volumetric measurement capabilities of the system, a simulated synovial tissue phantom with an embedded synovial effusion was fabricated and imaged. Ten CMC1 OA patients were imaged by our 3-D US and a 3.0 T MRI system to compare synovial volumes. The synovial volumes were manually segmented by two raters on the 2D slices of the 3D US reconstruction and MR images, to assess the accuracy and precision of the device for determining synovial tissue volumes. The Standard Error of Measurement and Minimal Detectable Change was used to assess the precision and sensitivity of the volume measurements. Paired sample t-tests were used to assess statistical significance. Additionally, rater reliability was assessed using Intra-Class Correlation (ICC) coefficients. The largest percent difference observed between the known physical volume of synovial extrusion in the phantom and the volume measured by our 3D US was 1.1% (p-value=0.03). The mean volume difference between the 3-D US and the gold standard MRI was 1.78% (p-value=0.48). The 3-D US synovial tissue volume measurements had a Standard Error Measurement (SEm ) of 11.21mm3 and a Minimal Detectible Change (MDC) of 31.06mm3 , while the MRI synovial tissue volume measurements had an SEM of 16.82mm3 and an MDC of 46.63mm3 . Excellent inter- and intra-rater reliability (ICCs=0.94-0.99) observed across all imaging modalities and raters. Our results indicate the feasibility of applying 3-D US technology to provide accurate and precise CMC1 synovial tissue volume measurements, similar to MRI volume measurements. Lower MDC and SEm values for 3-D US volume measurements indicate that it is a precise measurement tool to assess synovial volume and that it is sensitive to variation between volume segmentations. The application of this imaging technique to monitor OA pathogenesis and treatment response over time at the patient's bedside should be thoroughly investigated in future studies.

Weakly supervised learning-based 3D bladder reconstruction from 2D ultrasound images for bladder volume measurement.

Accurate measurement of bladder volume is necessary to maintain the consistency of the patient's anatomy in radiation therapy for pelvic tumors. As the diversity of the bladder shape, traditional methods for bladder volume measurement from 2D ultrasound have been found to produce inaccurate results. To improve the accuracy of bladder volume measurement from 2D ultrasound images for patients with pelvic tumors. The bladder ultrasound images from 130 patients with pelvic cancer were collected retrospectively. All data were split into a training set (80 patients), a validation set (20 patients), and a test set (30 patients). A total of 12 transabdominal ultrasound images for one patient were captured by automatically rotating the ultrasonic probe with an angle step of 15°. An incomplete 3D ultrasound volume was synthesized by arranging these 2D ultrasound images in 3D space according to the acquisition angles. With this as input, a weakly supervised learning-based 3D bladder reconstruction neural network model was built to predict the complete 3D bladder. The key point is that we designed a novel loss function, including the supervised loss of bladder segmentation in the ultrasound images at known angles and the compactness loss of the 3D bladder. Bladder volume was calculated by counting the number of voxels belonging to the 3D bladder. The dice similarity coefficient (DSC) was used to evaluate the accuracy of bladder segmentation, and the relative standard deviation (RSD) was used to evaluate the calculation accuracy of bladder volume with that of computed tomography (CT) images as the gold standard. The results showed that the mean DSC was up to 0.94 and the mean absolute RSD can be reduced to 6.3% when using 12 ultrasound images of one patient. Further, the mean DSC also was up to 0.90 and the mean absolute RSD can be reduced to 9.0% even if only two ultrasound images were used (i.e., the angle step is 90°). Compared with the commercial algorithm in bladder scanners, which has a mean absolute RSD of 13.6%, our proposed method showed a considerably huge improvement. The proposed weakly supervised learning-based 3D bladder reconstruction method can greatly improve the accuracy of bladder volume measurement. It has great potential to be used in bladder volume measurement devices in the future.

A differential detailed diffusion-weighted imaging-ASPECTS for cerebral infarct volume measurement and outcome prediction.

Diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) has been used to estimate infarct core volume in acute stroke. However, the same and indiscriminate score deduction for punctate or confluent DWI high-intensity lesion might lead to variation in performance. To develop and evaluate a differential detailed DWI-ASPECTS method in comparison with the conventional DWI-ASPECTS in core infarct volume measurement and clinical outcome prediction. We retrospectively recruited patients with acute ischemic stroke (AIS) treated with endovascular treatment between April 2013 and October 2019. In differential detailed DWI-ASPECTS, restricted diffusion lesion that was punctate or less than half of a cortical region (M1-M6) would not lead to subtraction of point. A favorable outcome was modified Rankin Scale score ⩽2 at 90 days after stroke onset. Among 298 AIS patients, mean age was 75 years (interquartile range (IQR) 67-82), and 194 patients (65%) were males. Mean infarct core volume was 11 mL (IQR 3-37). Overall, the score by detailed DWI-ASPECTS was significantly higher than conventional DWI-ASPECTS (8 (7-9) vs. 7 (5-9); P < 0.01). The detailed DWI-ASPECTS resulted in a higher correlation coefficient (r) for core infarct volume estimation than the conventional DWI-ASPECTS (r = 0.832 vs. 0.773; P < 0.01). Upon re-classification of those scored ⩽6 in conventional DWI-ASPECTS (n = 134) by detailed DWI-ASPECTS, the rate of favorable outcome in patients with detailed DWI-ASPECTS >6 was significantly higher than those with ⩽6 (29 (48%) vs. 14 (19%); P < 0.01). Detailed DWI-ASPECTS appeared to provide a more accurate infarct core volume measurement and clinical outcome correlation than conventional DWI-ASPECTS among AIS patients treated with endovascular therapy.

Marine mammal morphometrics: 3D modeling and estimation validation

Techniques of 3D modeling have earned increasing popularity in scientific studies as they offered unprecedented traits in representing objects. As with all mathematical models, the 3D model will be useful once its accuracy has been validated with direct measurements and the robustness of its predictive capability tested. Although measures of body mass and volume are essential to comprehend the life history of animals, such morphometrics, especially total volume, are challenging to obtain from marine mammals due to their elusive nature, aquatic lifestyle, and large size. In this study, accurate measurements of body volume were directly detected from fresh carcasses of eleven finless porpoises (Neophocaena asiaeorientalis sunameri) and used to validate 3D models recreated from the same animals using Blender 3D graphics. Published models using truncated cones or elliptical shapes, based on 3, 5, 8, or 19 measurements of girths or heights &amp;amp; widths along the body, were also applied to porpoises to verify their accuracy. The Blender-generated 3D model produced the most accurate estimates of body volume compared to conventional truncated models, with a mean error of only 2.5% to the direct volume measurements. When photogrammetric images are available, the model can predict the body volumes based on total length alone. Similar accuracy was possible with the elliptical model using 19 height &amp;amp; width measurements (5% increments in total length). However, significant (p &amp;lt; 0.001) inaccuracy resulted from truncated models with 3, 5, or 8 girth measurements and elliptical models with 3 or 5 height &amp;amp; width measurements, and the accuracy of these models also decreased significantly as the number of measurement sites was reduced. Moreover, Blender 3D models can be extrapolated to animals with images absent, and such predicted volumes were tested to be trustworthy (overall skill = 0.998, r = 0.998, p &amp;lt; 0.01). Therefore, we recommend that researchers use either digital 3D models or elliptical models with 5% increments sectioning the torso to obtain accurate estimates of the body volumes of free-ranging marine mammals.

Computed Tomography scanning in patients with COVID-19: artificial intelligence analysis of lesions volume and outcome.

The aim of this study was to summarize the computed tomography (CT) chest scanning results of COVID-19 patients, and to assess the value of artificial intelligence (AI) dynamics and quantitative analysis of lesion volume change for the evaluation of the disease outcome. First chest CT and reexamination imaging data of 84 patients diagnosed with COVID-19 who were treated at Jiangshan Hospital of Guiyang, Guizhou Province from February 4, 2020, to February 22, 2020, were retrospectively analyzed. Distribution, location, and nature of lesions were analyzed according to the characteristics of CT imaging and COVID-19 diagnosis and treatment guidelines. Based on the results of the analysis, patients were divided into the group without abnormal pulmonary imaging, the early group, the rapid progression group, and the dissipation group. AI software was used to dynamically measure the lesion volume in the first examination and in the cases with more than two reexaminations. There were statistically significant differences in the age of patients between the groups (p<0.01). The first chest CT examination of the lung without abnormal imaging findings mainly occurred in young adults. Early and rapid progression was more common in the elderly, with a median age of 56 years. The ratio of the lesion to the total lung volume was 3.7 (1.4, 5.3) ml 0.1%, 15.4 (4.5, 36.8) ml 0.3%, 115.0 (44.5, 183.3) ml 3.33%, 32.6 (8.7, 98.0) ml 1.22% in the non-imaging group, early group, rapid progression group, and dissipation group, respectively. Pairwise comparison between the four groups was statistically significant (p<0.001). AI measured the total volume of pneumonia lesions and the proportion of the total volume of pneumonia lesions to predict the receiver operating characteristic (ROC) curve from early development to rapid progression, with a sensitivity of 92.10%, 96.83%, specificity of 100%, 80.56%, and the area under the curve of 0.789. Accurate measurement of lesion volume and volume changes by AI technology is helpful in assessing the severity and development trend of the disease. The increase in the lesion volume proportion indicates that the disease has entered a rapid progression period and is aggravated.

Semi-supervised deep neural networks for left atrial segmentation from cardiac MRI

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Left atrium size and function is an important but less studied prognostic marker for many cardiovascular diseases [1]. Cardiac Magnetic Resonance Imaging (MRI) can capture atrial structure with a high temporal and spatial resolution. However, diagnoses and accurate measurement of volumes from MRI often rely on time-consuming manual processing, or needs correction of software segmentation error by clinicians. Robust, automated methods are highly desirable to assist morphological analysis of dynamic changes in the heart. Ultimately this facilitates the wider application of cardiac MRI in early identification and prevention of potential cardiovascular disease. Purpose To introduce a semi-supervised teacher-student deep neural networks (requiring less labelled data) for accurate segmentation of left atrium from cardiac MRI images, and compare their performance with conventional supervised learning. Methods The basic structure for a mean teacher deep neural network is shown in Fig.1. Network training starts in a supervised manner. At each MRI batch input, both the student and the teacher model evaluate the images with applied noise. The student network is updated by gradient descent and segmentation loss is obtained by comparing its prediction with the label. The exponential moving average (EMA) of the student weight is then migrated to update the teacher network. Consistency loss between the student’s and teacher’s prediction (filtered by uncertainty map in Uncertainty-Aware Mean Teacher, UA-MT [2]) is obtained to jointly update the student. Unlabeled images continually improve the network without using supervised loss. We compared three semi-supervised teacher-student networks UA-MT, Hierarchical Consistency Regularized Mean Teacher (HCR-MT) and Semi-supervised Contrastive Consistency model (SCC), with the supervised network TransUNnet [3], using benchmark cardiac MRI dataset from MICCAI 2018 Atrial Segmentation Challenge [4]. Out of 20 MRI scans (1760 images), 16 scans were used for training, and 4 scans for testing. For semi-supervised learning, 75% of the training set was used as labelled data and 25% was assumed to be unlabeled. In the supervised learning, all labels were enabled. Results Example segmentation results from three semi-supervised models are shown in Fig 2. For comparison, the fully supervised TransUNnet produced an accuracy of 76%. Benefiting from effective teacher-student learning, the semi-supervised models achieved a superior accuracy of 89%, while also requiring less labelled data. Conclusion The proposed teacher-student deep neural network facilitates effective semi-supervised learning, and outperforms supervised TransUNet in segmenting the left atrium in cardiac MRI. Our semi-supervised approach uses less training data, reducing tedious and time-consuming manual labelling tasks, ultimately unlocking the ability to effectively utilise the full power of deep learning in medical applications.