scholarly journals Counting Mixed Breeding Aggregations of Animal Species Using Drones: Lessons from Waterbirds on Semi-Automation

2020 ◽  
Vol 12 (7) ◽  
pp. 1185 ◽  
Author(s):  
Roxane J. Francis ◽  
Mitchell B. Lyons ◽  
Richard T. Kingsford ◽  
Kate J. Brandis
Keyword(s):  
Animal Species ◽  
Okavango Delta ◽  
Detection Accuracy ◽  
Counting Method ◽  
Breeding Colony ◽  
General Applicability ◽  
Automated Method ◽  
Waterbird Species ◽  
Manual Counting ◽  
Automated Methods

Using drones to count wildlife saves time and resources and allows access to difficult or dangerous areas. We collected drone imagery of breeding waterbirds at colonies in the Okavango Delta (Botswana) and Lowbidgee floodplain (Australia). We developed a semi-automated counting method, using machine learning, and compared effectiveness of freeware and payware in identifying and counting waterbird species (targets) in the Okavango Delta. We tested transferability to the Australian breeding colony. Our detection accuracy (targets), between the training and test data, was 91% for the Okavango Delta colony and 98% for the Lowbidgee floodplain colony. These estimates were within 1–5%, whether using freeware or payware for the different colonies. Our semi-automated method was 26% quicker, including development, and 500% quicker without development, than manual counting. Drone data of waterbird colonies can be collected quickly, allowing later counting with minimal disturbance. Our semi-automated methods efficiently provided accurate estimates of nesting species of waterbirds, even with complex backgrounds. This could be used to track breeding waterbird populations around the world, indicators of river and wetland health, with general applicability for monitoring other taxa.

Feasibility and Evaluation of Automated Methods for Radiolabeling of Radiopharmaceutical Kits with Gallium-68

2019 ◽  
Vol 12 (3) ◽  
pp. 229-237 ◽  
Author(s):  
Alban Revy ◽  
François Hallouard ◽  
Sandrine Joyeux-Klamber ◽  
Andrea Skanjeti ◽  
Catherine Rioufol ◽  
...  
Keyword(s):  
The European Union ◽  
Preparation Time ◽  
Limit Of Quantification ◽  
Manual Method ◽  
Personnel Costs ◽  
Automated Method ◽  
Gallium 68 ◽  
The Right ◽  
First Time ◽  
Automated Methods

Objective: Recent gallium-68 labeled peptides are of increasing interest in PET imaging in nuclear medicine. Somakit TOC® is a radiopharmaceutical kit registered in the European Union for the preparation of [68Ga]Ga-DOTA-TOC used for the diagnosis of neuroendocrine tumors. Development of a labeling process using a synthesizer is particularly interesting for the quality and reproducibility of the final product although only manual processes are described in the Summary of Product (SmPC) of the registered product. The aim of the present study was therefore to evaluate the feasibility and value of using an automated synthesizer for the preparation of [68Ga]Ga-DOTA-TOC according to the SmPC of the Somakit TOC®. Methods: Three methods of preparation were compared; each followed the SmPC of the Somakit TOC®. Over time, overheads, and overexposure were evaluated for each method. Results: Mean±SD preparation time was 26.2±0.3 minutes for the manual method, 28±0.5 minutes for the semi-automated, and 40.3±0.2 minutes for the automated method. Overcost of the semi-automated method is 0.25€ per preparation for consumables and from 0.58€ to 0.92€ for personnel costs according to the operator (respectively, technician or pharmacist). For the automated method, overcost is 70€ for consumables and from 4.06€ to 6.44€ for personnel. For the manual method, extremity exposure was 0.425mSv for the right finger, and 0.350mSv for the left finger; for both the semi-automated and automated method extremity exposure were below the limit of quantification. Conclusion: The present study reports for the first time both the feasibility of using a [68Ga]- radiopharmaceutical kit with a synthesizer and the limits for the development of a fully automated process.

Collaborative Study of Turbidimetric Microbiological Assay of Monensin in Chicken Rations

1977 ◽  
Vol 60 (1) ◽  
pp. 179-182
Author(s):  
H Latham Breunig ◽  
Robert E Scroggs ◽  
Lealon V Tonkinson ◽  
Henry Bikin
Keyword(s):  
Experimental Design ◽  
Collaborative Study ◽  
Microbiological Assay ◽  
Assay Method ◽  
Turbidimetric Method ◽  
Automated Method ◽  
Relative Standard ◽  
Significant Difference ◽  
Feed Form ◽  
Automated Methods

Abstract A turbidimetric microbiological assay method for monensin in chicken rations was submitted in a modified form to 8 collaborating laboratories along with randomized and coded samples. Three laboratories used the manual method and 5 used the automated method. Other factors in the experimental design were ration types (broiler starter, broiler finisher, and pullet grower), feed form (meal vs. pellets), and potency level (90 and 110 g/ton) for one ration. Average recoveries for the ration types over all laboratories and feed forms were 87.7—93.13% of label, while mean recoveries in 2 feed forms were 91.7% for meal and 87.6% for pellets. Average recoveries in the 8 laboratories ranged from 84.6 to 106.64% of label for 90 g/ton rations and 87.1 to 106.6% for 110 g/ton rations. There was no significant difference between the manual and the automated methods. The collaborators’ assays were uniform with respect to within-laboratory variation. Relative standard deviations ranged from 4.51 to 10.76% with a median of 6.04%. Agreement with the plate assay is quite good. The turbidimetric method for monensin has been adopted as official first action.

Detecting and Identifying Spectral Anomalies Using Wavelet Processing

1998 ◽  
Vol 52 (10) ◽  
pp. 1348-1352 ◽  
Author(s):  
Chris L. Stork ◽  
David J. Veltkamp ◽  
Bruce R. Kowalski
Keyword(s):  
Wavelet Transform ◽  
Near Infrared ◽  
Principal Component ◽  
Basis Set ◽  
Multivariate Statistical ◽  
General Applicability ◽  
Statistical Process ◽  
Automated Method ◽  
Infrared Measurements ◽  
Wavelength Scale

An automated method integrating wavelet processing and techniques from multivariate statistical process control (MSPC) is presented, providing a means for the simultaneous localization, detection, and identification of disturbances in spectral data. A defining property of the wavelet transform is its ability to map a one-dimensional chemical spectrum into a two-dimensional function of wavelength and scale. Therefore, unlike the traditional MSPC approach where disturbance detection is carried out in the original wavelength domain by using a single principal component analysis (PCA) model, detection employing wavelet transform processing results in the generation of multiple models within the wavelength-scale domain. Provided that the spectral disturbance can be localized within a subregion of the wavelength-scale domain through an advantageous choice of basis set, the method described allows the identification of the underlying disturbance. The utility of the proposed method in localizing, detecting, and identifying spectral disturbances is demonstrated by using real near-infrared measurements, suggesting its general applicability in spectroscopic monitoring of chemical processes.

scholarly journals Comparison of galaxy spiral arm pitch angle measurements using manual and automated techniques

2020 ◽  
Vol 493 (3) ◽  
pp. 3854-3865
Author(s):  
Ian B Hewitt ◽  
Patrick Treuthardt
Keyword(s):  
Pitch Angle ◽  
Spiral Galaxies ◽  
Small Sample ◽  
Automated Method ◽  
Angle Measurements ◽  
Barred Spiral Galaxy ◽  
Manual Input ◽  
Automated Methods ◽  
Disc Galaxies ◽  
Spiral Arm

ABSTRACT The pitch angle (PA) of arms in spiral galaxies has been found to correlate with a number of important parameters that are normally time intensive and difficult to measure. Accurate PA measurements are therefore important in understanding the underlying physics of disc galaxies. We introduce a semi-automated method that improves upon a parallelized two-dimensional fast Fourier transform algorithm (p2dfft) to estimate PA. Rather than directly inputting deprojected, star subtracted, and galaxy centred images into p2dfft, our method (p2dfft:traced) takes visually traced spiral arms from deprojected galaxy images as input. The tracings do not require extensive expertise to complete. This procedure ignores foreground stars, bulge and/or bar structures, and allows for better discrimination between arm and interarm regions, all of which reduce noise in the results. We compare p2dfft:traced to other manual and automated methods of measuring PA using both simple barred and non-barred spiral galaxy models and a small sample of observed spiral galaxies with different representative morphologies. We find that p2dfft:traced produces results that, in general, are more accurate and precise than the other tested methods and it strikes a balance between total automation and time-consuming manual input to give reliable PA measurements.

Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network

2020 ◽  
pp. 1-16
Author(s):  
Ling Zhang ◽  
Yan Zhuang ◽  
Zhan Hua ◽  
Lin Han ◽  
Cheng Li ◽  
...  
Keyword(s):  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Ultrasound Images ◽  
Semantic Features ◽  
Low Contrast ◽  
Automated Method

BACKGROUND: Thyroid ultrasonography is widely used to diagnose thyroid nodules in clinics. Automatic localization of nodules can promote the development of intelligent thyroid diagnosis and reduce workload of radiologists. However, besides the ultrasound image has low contrast and high noise, the thyroid nodules are diverse in shape and vary greatly in size. Thus, thyroid nodule detection in ultrasound images is still a challenging task. OBJECTIVE: This study proposes an automatic detection algorithm to locate nodules in B ultrasound images and Doppler ultrasound images. This method can be used to screen thyroid nodules and provide a basis for subsequent automatic segmentation and intelligent diagnosis. METHODS: We develop and optimize an improved YOLOV3 model for detecting thyroid nodules in ultrasound images with B-mode and Doppler mode. Improvements include (1) using the high-resolution network (HRNet) as the basic network for gradually extracting high-level semantic features to reduce the missed detection and misdetection, (2) optimizing the loss function for single target detection like nodules, and (3) obtaining the anchor boxes by clustering the candidate frames of real nodules in the dataset. RESULTS: The experimental results of applying to 8000 clinical ultrasound images show that the new method developed and tested in this study can effectively detect thyroid nodules. The method achieves 94.53% mean precision and 95.00% mean recall. CONCLUTIONS: The study demonstrates a new automated method that enables to achieve high detection accuracy and effectively locate thyroid nodules in various ultrasound images without any user interaction, which indicates its potential clinical application value for the thyroid nodule screening.

Intelligent Measurement of Spinal Curvature Using Cascade Gentle AdaBoost Classifier and Region-Based DRLSE

2019 ◽  
Vol 23 (3) ◽  
pp. 502-511
Author(s):  
Liyuan Zhang ◽  
◽  
Jiashi Zhao ◽  
Zhengang Jiang ◽  
Huamin Yang
Keyword(s):  
Negative Impact ◽  
Ground Truth ◽  
Spinal Curvature ◽  
Training Dataset ◽  
Initial Contour ◽  
Detection Accuracy ◽  
Automated Method ◽  
Spine Ct ◽  
Vertebral Bodies ◽  
Adaboost Classifier

For spinal curvature measurements, because of the anatomical complexity of the spine CT image, developing an automated method to avoid manual landmark is a challenging task. In this study, we propose an intelligent framework that integrates the cascade AdaBoost classifier and region-based distance regularized level set evolution (DRLSE) with the vertebral centroid measurement. First, the histogram-of-oriented-gradients based cascade gentle AdaBoost classifier is used to detect automatically and localize vertebral bodies from computer tomography (CT) spinal images. Considering these vertebral pathological images enables us to produce a diverse training dataset. Then, the DRLSE method introduces the local region information to converge the vertebral boundary quickly. The located bounding box is regarded as an accurate initial contour. This avoids the negative impact of manual initialization. Finally, we perform vertebral centroid extraction and spinal curve fitting. The spinal curvature angle is determined by calculating the angle between two tangents to the curve. We verified the effectiveness of the proposed method on 10 spine CT volumes. Quantitative comparison against the ground-truth centroids yielded a detection accuracy rate of 98.3% and a mean centroid location error of 1.15 mm. The comparative results with existing methods demonstrate that the proposed method can accurately detect and segment vertebral bodies. Furthermore, the spinal curvature can be automatically measured without manual landmark.

Automated measurement of vessel properties in birch and poplar wood

Holzforschung ◽  
2010 ◽  
Vol 64 (3) ◽  
Author(s):  
Fang Fiona Chen ◽  
Robert Evans
Keyword(s):  
Red Light ◽  
Wood Property ◽  
Digital Microscopy ◽  
Core Component ◽  
Automated Method ◽  
Manual Counting ◽  
Wide Range ◽  
Property Measurement ◽  
Processing Techniques ◽  
Microscopy Images

Abstract A method is proposed for the automated quantitative analysis of vessel characteristics in birch and poplar species. The method combines image-processing techniques with robust statistical approaches for automatically identifying vessels from digital microscopy images obtained by transmitted red light. The proposed method has been tested over a wide range of birch and poplar samples from different growth environments. Performance of the automatic vessel identification routine was assessed using results obtained by manual counting. The automated method produced fast and reliable vessel measurements and was robust to variations within and between samples. The approach has been merged into the wood property measurement system SilviScan as a core component of the hardwood analysis set for research and commercial use.

scholarly journals IDENTIFIKASI BAKTERI AEROB GRAM NEGATIF DAN GRAM POSITIF MENGGUNAKAN METODE KONVENSIONAL DAN OTOMATIK

2018 ◽  
Vol 19 (2) ◽  
pp. 105
Author(s):  
Patricia M. Tauran ◽  
Irda Handayani ◽  
Nurhayana Sennang
Keyword(s):  
Aerobic Bacteria ◽  
Gram Negative Bacteria ◽  
Bacterial Taxonomy ◽  
Biochemical Tests ◽  
Gram Positive ◽  
Gram Negative ◽  
Gram Positive Bacteria ◽  
Automated Method ◽  
Automatic Methods ◽  
Automated Methods

Choosing the method of bacteria identification is crucial to obtain accurate and quick results. This study will analyze the identificationresults of Gram negative and Gram positive from aerobic bacteria by examination using conventional and automatic methods at Dr.Wahidin Sudirohusodo Hospital Laboratory. A total of 85 samples consisting of 66 Gram negative bacteria and 19 Gram positive bacteriawere identified using conventional and automated methods. In this study, there was some correspondent identification result betweenthe conventional as well as the automated methods, namely 31.5% for Gram negative bacteria and 30.8% for Gram positive bacteria.However, the non-correspondent identification result between conventional and automated methods was found greater, namely, 68.5%for Gram negative bacteria and 69.2% for Gram positive bacteria. The non-correspondent identification result was due to the developmentof bacterial taxonomy and the differences of numbers and types of the biochemical tests between conventional and automatic methods.Bacteria identification using automated method is more accurate and faster than the conventional method, so it is recommended usingthis particularly for the laboratory and educational referral center.

Total and direct-reacting bilirubin values by automated methods compared with liquid chromatography and with manual methods for determining delta bilirubin

1990 ◽  
Vol 36 (5) ◽  
pp. 788-791 ◽  
Author(s):  
P Rosenthal ◽  
M T Keefe ◽  
D Henton ◽  
M Cheng ◽  
C R Lee ◽  
...  
Keyword(s):  
Liquid Chromatography ◽  
Bile Acids ◽  
Total Bilirubin ◽  
Clinical Use ◽  
Serum Samples ◽  
Manual Method ◽  
Automated Method ◽  
Manual Methods ◽  
Automated Methods ◽  
Conjugated Bilirubin

Abstract This study compares total and direct-reacting bilirubin values in 40 serum samples from patients with various diagnoses, as measured by automated methods (Beckman Synchron CX-5, Beckman Astra 8, Kodak Ektachem 700) and HPLC and by a manual method for delta bilirubin. For total bilirubin, within-run CVs were less than 6%. The Ektachem 700 method underestimated bilirubin with serum samples from patients with Crigler-Najjar syndrome and from newborns in whom unconjugated bilirubin concentrations were increased but conjugated bilirubins were not present or were present only in small amounts. The Astra 8 and Synchron CX-5 methods were inaccurate with cholestatic serum samples, in which conjugated bilirubin concentrations were increased and other compounds such as bile acids could be expected to interfere. We conclude that each automated method examined provides reasonable estimates for total and direct-reacting bilirubin values for routine clinical use. The need for each laboratory to select the appropriate bilirubin method for its particular situation is obvious.

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