PPO.25 Novel insights from using Stereology based volume estimation of Syncytial nuclear aggregates in Diabetic placenta

Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.

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Quantitative evaluation and comparison of coronary artery characteristics by 3D coronary volume reconstruction

Scientific Reports ◽

10.1038/s41598-020-80928-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yongcheol Kim ◽

Jonathan James Hyett Bray ◽

Benjamin Waterhouse ◽

Alexander Gall ◽

Georgia May Connolly ◽

...

Keyword(s):

Coronary Artery ◽

Coronary Angiography ◽

Coronary Arteries ◽

Intraclass Correlation ◽

Quantitative Coronary Angiography ◽

Volume Estimation ◽

Diabetic Patients ◽

Volume Reconstruction ◽

3 Dimensional ◽

Inter Observer Variability

AbstractNon-atherosclerotic abnormalities of vessel calibre, aneurysm and ectasia, are challenging to quantify and are often overlooked in qualitative reporting. Utilising a novel 3-dimensional (3D) quantitative coronary angiography (QCA) application, we have evaluated the characteristics of normal, diabetic and aneurysmal or ectatic coronary arteries. We selected 131 individuals under 50 years-of-age, who had undergone coronary angiography for suspected myocardial ischaemia between 1st January 2011 and 31st December 2015, at the Bristol Heart Institute, Bristol, UK. This included 42 patients with angiographically normal coronary arteries, 36 diabetic patients with unobstructed coronaries, and 53 patients with abnormal coronary dilatation (aneurysm and ectasia). A total of 1105 coronary segments were analysed using QAngio XA 3D (Research Edition, Medis medical imaging systems, Leiden, The Netherlands). The combined volume of the major coronary arteries was significantly different between each group (1240 ± 476 mm3 diabetic group, 1646 ± 391 mm3 normal group, and 2072 ± 687 mm3 abnormal group). Moreover, the combined coronary artery volumes correlated with patient body surface area (r = 0.483, p < 0.01). Inter-observer variability was assessed and intraclass correlation coefficient of the total coronary artery volume demonstrated a low variability of 3D QCA (r = 0.996, p < 0.001). Dedicated 3D QCA facilitates reproducible coronary artery volume estimation and allows discrimination of normal and diseased vessels.

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An approximate point-based alternative for the estimation of variance under big BAF sampling

Forest Ecosystems ◽

10.1186/s40663-021-00304-0 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Thomas B. Lynch ◽

Jeffrey H. Gove ◽

Timothy G. Gregoire ◽

Mark J. Ducey

Keyword(s):

Forest Inventory ◽

Variance Estimation ◽

Basal Area ◽

Volume Estimation ◽

Delta Method ◽

Sampling Point ◽

Variance Estimator ◽

Computer Simulation Studies ◽

New Formula ◽

Estimation Of Variance

Abstract Background A new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes Bitterlich sampling (point sampling) with two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation. Methods The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula. Results Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas. Conclusions A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition. We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of $\frac {1}{n}$ 1 n where n is the number of sample points.

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Right ventricular ejection fraction as predictor of outcome in acute heart failure using RV ellipsoid model: A retrospective analysis of a prospective cross-sectional study

JRSM Cardiovascular Disease ◽

10.1177/20480040211002775 ◽

2021 ◽

Vol 10 ◽

pp. 204800402110027

Author(s):

Eshan Ashcroft ◽

Otar Lazariashvili ◽

Jonathan Belsey ◽

Max Berrill ◽

Pankaj Sharma ◽

...

Keyword(s):

Heart Failure ◽

Ejection Fraction ◽

Right Ventricular ◽

Retrospective Analysis ◽

Cross Sectional Study ◽

Volume Estimation ◽

Sectional Study ◽

Cross Sectional ◽

Ventricular Ejection Fraction ◽

Rv Function

Objectives The right ventricular (RV) function is an important prognostic factor in acute and chronic heart failure (HF). Echocardiography is an essential imaging modality with established parameters for RV function which are useful and easy to perform. However, these fail to reflect global RV volumes due to reliability on one acoustic window. It is therefore attractive to calculate RV volumes and ejection fraction (RVEF/E) using an ellipsoid geometric model which has been validated against MRI in healthy adults but not in the HF patients. Design This is a retrospective analysis of a prospective cross-sectional study enrolling 418 consecutive patients with symptoms of HF according to a predefined study protocol. All patients underwent echocardiographic assessment of RV function using Tricuspid Annular Plane Systolic Excursion (TAPSE) and RV fractional area change (RVFAC) and RVEF/E. Setting Single centre study with multiple locations for acute in-patients including high dependency units. Participants Patients with acute or exacerbation of chronic HF older than 18 y.o. Main outcome measures Ability of RVEF/E to predict patient outcomes compared with two established parameters of RV function over two-year follow-up period. Primary outcome measure was all-cause mortality. Results RVEF/E is equal to TAPSE & RVFAC in predicting outcome (p ≤ 0.01 vs p ≤ 0.01) and provides additional benefit of RV volume estimation based on standard 2D echo measurements. Conclusions In this study we have shown that RVEF/E derived from ellipsoid model is not inferior to well established measures of RV function as a prognostic indicator of outcome in the acute HF.

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Vol-Net and V3C-Net: Towards End to End Traffic Volume Estimation

2021 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas51556.2021.9401238 ◽

2021 ◽

Author(s):

Hood Khizer ◽

Wahaj Ahmad ◽

Tayyba Naz ◽

Hasan Sajid

Keyword(s):

Traffic Volume ◽

Volume Estimation ◽

End To End ◽

Traffic Volume Estimation

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Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System

Electronics ◽

10.3390/electronics10131556 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1556

Author(s):

Zhengeng Yang ◽

Hongshan Yu ◽

Shunxin Cao ◽

Qi Xu ◽

Ding Yuan ◽

...

Keyword(s):

Chronic Diseases ◽

Volume Estimation ◽

Portion Size ◽

Inner Product ◽

Probability Vector ◽

Internal Reference ◽

Single View ◽

Unhealthy Diet ◽

Simplified Procedure ◽

Traditional Image

It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of performing objective and passive dietary assessments with a much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms. In this work, we present a novel Artificial Intelligent (AI) system to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system “mentally” gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an “intelligent guess”, implemented by an inner product between the probability vector and the reference volume vector. Our experiments using both virtual and real food datasets have shown accurate volume estimation results.

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