Automatic Counting System Research Articles

A novel fully automatic segmentation and counting system for metastatic lymph nodes on multimodal magnetic resonance imaging: Evaluation and prognostic implications in nasopharyngeal carcinoma

BackgroundThe number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting. MethodsWe retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard). ResultsIn the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1–4, 5–9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort. ConclusionsThe AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection.

Development of a Web-Based Automated Production Inventory System

This research focuses on the design of an automatic counting system for production items using the ESP32 microcontroller and E18-D80NK sensor. The main objective of the study is to address inventory issues, particularly in the context of manual counting of production items, which is susceptible to shortages and inaccuracies. The proposed automatic counting device aims to provide a solution to enhance employee performance efficiency and reduce the risk of human errors. The integration of the ESP32 microcontroller as a data processor and the E18-D80NK sensor as an information capture device will allow easy access to calculation results through a website interface. The primary advantage of this system is its ability to prevent potential data forgery in production and provide more accurate calculation results. With data storage on a website platform, tracking production history becomes more effective, enabling in-depth analysis of overall production performance. This research proposes an integrated solution to enhance efficiency, transparency, and accuracy in the production counting process. With the implementation of this technology, it is expected that the automated production system can be a strategic step towards improving productivity and the reliability of production information.

U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting.

In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination platform as an image capturing system to obtain photographs of laboratory cultures. Consequently, a dataset was introduced consisting of 390 photos of agar plate cultures, which included 8 microorganisms. Secondly, we implemented a new algorithm for image preprocessing based on light intensity correction, which facilitated clearer differentiation between colony and media areas. Thirdly, a U2-Net was used to predict the probability distribution of the edge of the Petri dish in images to locate region of interest (ROI), and then threshold segmentation was applied to separate it. This U2-Net achieved an F1 score of 99.5% and a mean absolute error (MAE) of 0.0033 on the validation set. Then, another U2-Net was used to separate the colony region within the ROI. This U2-Net achieved an F1 score of 96.5% and an MAE of 0.005 on the validation set. After that, the colony area was segmented into multiple components containing single or adhesive colonies. Finally, the colony components (CC) were innovatively rotated and the image crops were resized as the input (with 14,921 image crops in the training set and 4281 image crops in the validation set) for the ResNet50 network to automatically count the number of colonies. Our method achieved an overall recovery of 97.82% for colony counting and exhibited excellent performance in adhesion classification. To the best of our knowledge, the proposed "light intensity correction-based image preprocessing→U2-Net segmentation for Petri dish edge→U2-Net segmentation for colony region→ResNet50-based counting" scheme represents a new attempt and demonstrates a high degree of automation and accuracy in recognizing and counting single-colony and multi-colony targets.

An automatic cough counting method and system construction for portable devices.

Cough is a common symptom of respiratory diseases, and prolonged monitoring of cough can help assist doctors in making judgments about patients' conditions, among which cough frequency is an indicator that characterizes the state of the patient's lungs. Therefore, the aim of this paper is to design an automatic cough counting system to monitor the number of coughs per minute for a long period of time. In this paper, a complete cough counting process is proposed, including denoising, segment extraction, eigenvalue calculation, recognition, and counting process; and a wearable automatic cough counting device containing acquisition and reception software. The design and construction of the algorithm is based on realistically captured cough-containing audio from 50 patients, combined with short-time features, and Meier cepstrum coefficients as features characterizing the cough. The accuracy, sensitivity, specificity, and F1 score of the method were 93.24%, 97.58%, 86.97%, and 94.47%, respectively, with a Kappa value of 0.9209, an average counting error of 0.46 counts for a 60-s speech segment, and an average runtime of 2.80 ± 2.27s. This method improves the double threshold method in terms of the threshold and eigenvalues of the cough segments' sensitivity and has better performance in terms of accuracy, real-time performance, and computing speed, which can be applied to real-time cough counting and monitoring in small portable devices with limited computing power. The developed wearable portable automatic cough counting device and the accompanying host computer software application can realize the long-term monitoring of patients' coughing condition.

NDMFCS: An automatic fruit counting system in modern apple orchard using abatement of abnormal fruit detection

Automatic fruit counting is an important task for growers to estimate yield and manage orchards. Although many deep-learning-based fruit detection algorithms have been developed to improve performance of automatic fruit counting systems, abnormal fruit detection has often been caused by these algorithms detecting non-target fruits that have similar growth characteristics to target fruits. For abnormal fruit detection, detected fruits in the back row of the tree were defined as DFBRT, while detected fruits on the ground were defined as DFG. Both of them would result in a higher number of fruits counting than the ground truth. This study proposes an automatic fruit counting system called NDMFCS (Normal Detection Matched Fruit Counting System) to solve this problem for improving fruit counting accuracy in modern apple orchard. NDMFCS consists of three sub-systems, i.e. object detection based on You Only Look Once Version 4-tiny (YOLOv4-tiny), abatement of abnormal fruit detection based on threshold, and fruit counting based on trunk tracking and identity document (ID) assignment. YOLOv4-tiny was selected to implement detection of fruits and trunks, whose output is confidence and pixel coordinates of detected object. The DFBRT and DFG were abated by thresholds to improve detection performance of fruit. This meant that detected fruits were removed when their distance from camera is further than a distance threshold or the confidence of fruit detection is less than a confidence threshold. Finally, fruit counting was implemented by trunk tracking and ID assignment, where each fruit was assigned a unique tracking ID. Results on 10 sets of original videos indicated that average fruit detection precision was improved from 89.1% to 93.3% after abatement of abnormal fruit detection. Also, Multiple Object Tracking Accuracy and Multiple Object Tracking Precision were improved on average by 4.2% and 3.3%, respectively, while average ID Switch Rate was decreased on average by 1.1%. And average fruit counting accuracy was improved to 95.0% by 4.2%. Coefficient of determination (R2) was 0.97, which indicated the number of fruits counted by NDMFCS was near to the ground truth. These results demonstrate that the abatement of abnormal fruit detection can improve performance of apple counting, which has the potential to provide a technical support for estimating fruit yield in modern apple orchards.

Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision

The development of computer technology today is very helpful for humans in completing their work in various fields. One application of computer technology i.e., in the field of computer vision which has a very important role for object recognition. In this study, we designed a computer vision-based automatic vehicle counting system. The system that we created uses the MobileNetV2 Single Shot Multibox Detector (SSD) which is placed on the Raspberry Pi 4 to carry out the process of classifying cars and motorcycles and the raspberry pi 4 also functions as a system controller. This automatic vehicle counter system has been integrated between Raspberry Pi 4 and a mobile application on a smartphone where the smartphone functions to display information such as day, date, month, year and together with the number of cars and motorcycles. We tested this automatic vehicle counting system on steam services (car and motorcycle washing) for 3 days where 10 vehicles were collected every day. The test results show that the system is capable of detecting cars and motorcyles with an average accuracy rate of 46.6%.

Wireless Sensor Network Architecture for Passenger Counting in Public Transportation

The paper presents a concept architecture of the sensor network, with the purpose of counting passengers in public transport. The architecture of the presented system is represented in the form of a block diagram. A concept architecture for a passenger automatic counting system based on a two-layer clustered mobile sensor network has been proposed. Each observed vehicle is considered as a cluster that transmits information to the central station via wireless communication. The vehicle contains two sensors based on the principle of Passive Infrared Sensors (PIR) principle. The vehicle also has an LCD display that shows the situation with passengers and a microcontroller that has a built-in counting algorithm based on passengers that have entered and exited. As a case study, the system for counting travellers in the public transport system of buses is given.

A Low-Cost Bidirectional People Counter Device for Assisting Social Distancing Monitoring for COVID-19

Accurately estimating the number of people is a useful information to monitor the occupancy level of spaces due to the COVID-19 pandemic in order to keep social distancing. Automated counters are responsible for accurately counting people movement. This paper proposes a cost-effective automatic counting system based on a microcontroller through an infrared sensor integration to monitor occupancy of indoor spaces. In order to evaluate the proposed system in real-world scenarios, experiments were carried out at the main campus of the University of Campinas—Brazil. For the first case, two prototypes were installed at both the front and rear door of a bus in order to count the number of passengers boarding and alighting on the vehicle during its operation. In the second case, three prototypes were installed in the university restaurant ticket gates in order to count the number of people entering during lunchtime. Experimental results showed an accuracy of 91.45% and 98.65% for Cases 1 and 2, respectively. The results are promising, showing that the device has potential to be used for different proposes, such as controlling access to public and indoor spaces, public transportation planning, occupancy monitoring, and security system.

Build coconut counting system using image technology

In our country today, the counting of dried coconuts at the production facilities is done manually, takes a lot of time and is not accurate. The goal of this study is to build an automatic, fast and accurate coconut counting system. The study was conducted on the peeled dried coconut fruit with a diameter of 15 cm to 20 cm using image processing technology and open-source computer vision library - OpenCV library. The algorithm includes four main steps. First, determine the object and the background using the Otsu segmentation method. Next, estimate the distance between the background and the object to determine the closest area to the center of the object. Then, find the contour, determine the center and area of the object to reduce the noise. The watershed segmentation algorithm is used to separate overlapping and stacking objects. Finally, count the number of objects contained in the image. In the initial experimental results, the counting system has had an accuracy of over 95% with processing time per image about 75 ms and the counting capacity of the system is over 2000 fruits/hour has confirmed the efficiency of the proposed method.

Association Between Pre-Treatment and Post-Treatment 3-Month Red Cell Distribution Width with Three-Year Prognosis of Prostate Cancer.

PurposeRed cell distribution width (RDW), an inflammation biomarker, has been linked to poor outcomes in patients with different types of cancers. The present study aimed to investigate the relationship between pre-/post-treatment 3-month RDW levels and changes in RDW with 3-year prognosis of prostate cancer.Patients and MethodsA total of 348 patients with prostate cancer were recruited between June 1, 2012 and June 1, 2017 and were followed up for at least 3 years. RDW was measured with the Mindray BC-6800Plus automatic blood counting system at pre- and post-treatment 3-month. Demographic and clinical information of the participants were also collected. The overall survival (OS) and cancer-specific survival (CSS) were analyzed using the Kaplan–Meier method. Cox regression and competing risk regression analyses were performed.ResultsDuring the follow-up period, 51 (14.66%) deaths occurred. The levels of pre- and post-treatment RDW levels were significantly higher in the death group than in the survival group (p<0.001). In the death group, the level of RDW continued to rise in most subjects, and the mean level of RDW was significantly higher at post-treatment than pre-treatment, contrary to the results observed in the survival group. Multivariate Cox regression analysis revealed that high pre-treatment RDW, high post-treatment RDW, and persistently higher RDW were independently associated with OS and CSS (p<0.001). Similar results were observed in the competing risk regression analysis. Kaplan–Meier analysis revealed that patients with higher pre-treatment RDW levels, higher post-treatment RDW levels, and persistently higher RDW levels had poorer 3-year OS and CSS rates (p<0.05).ConclusionThe levels of and changes in RDW before and after treatment were associated with the 3-year prognosis of prostate cancer, suggesting that RDW might be an efficient prognostic predictor in patients with prostate cancer.

An automatic system for pest recognition and forecasting.

Pests cause significant damage to agricultural crops and reduce crop yields. Use of manual methods of pest forecasting for integrated pest management is labor-intensive and time-consuming. Here, we present an automatic system for monitoring pests in large fields, with the aim of replacing manual forecasting. The system comprises an automatic detection and counting system and a human-computer data statistical fitting system. Image data sets of the target pests from large fields are first input into the system. The number of pests in the image is then counted both manually and using the automatic system. Finally, a mapping relationship between counts obtained using the automated system and by agricultural experts is established using the statistical fitting system. Trends in the pest-count curves produced using the manual and automated counting methods were very similar. To sample the number of pests for manual statistics, plants were shaken to transfer the pests from the plant to a plate. Hence, pests hiding within plant crevices were also sampled and included in the count, whereas the automatic method counted only the pests visible in the images. Therefore, the computer index threshold was much lower than the manual index threshold. However, the proposed system correctly reflected trends in pest numbers obtained using computer vision. The experimental results demonstrate that our automatic pest-monitoring system can generate pest grades and can replace manual forecasting methods in large fields. © 2021 Society of Chemical Industry.

A Quantitative Assessment of Indoor Radon Level and Its Annual Effective Dose in Buildings of Gachin Rural District in Hormozgan Province, Iran

Introduction: Measurement of indoor radon concentration and its determining factors is crucial for improving public health and developing proper methods that can reduce indoor radon concentrations. This study aimed to measure the indoor radon concentration and to examine its variations in relation to variables, such as the construction materials, ventilation, and age of buildings. Material and Methods: Indoor radon concentrations were measured using solid-state nuclear track detectors (SSNTDs) during winter. Each detector was mounted 50-90 cm above the surface flooring of bedrooms and living rooms. After three months of exposure, the detectors were collected and transferred to a laboratory. They were then etched in 6.25 N NaOH solution in a bath at a constant temperature of 90°C for 240 minutes. Next, the detectors were washed with distilled water and dried. The alpha particle tracks were counted using an automatic alpha track counting system. Results: The mean radon concentration was 53.20 Bq/m3,and 94% of the samples had a radon concentration 3, which is the action level proposed by the World Health Organization (WHO). The annual effective dose varied from 0.25 mSvy-1 to 3.05 mSvy-1, with a mean dose of 0.91 mSvy-1. The results showed that the type of constructed materials and ventilation influence the indoor radon concentration in winter. Conclusion: The annual effective dose in the study area was below the global average of 1.15 mSvy-1. Therefore, local residents must be informed about the health risks of high radon concentrations and understand the role of improved ventilation in reducing the indoor radon levels.

Monitoring bent-wing bats at Bat Cleft in Central Queensland

Undertaking accurate counts is an important part of managing cave-dwelling bats species such as the little bent-wing bat (Miniopterus australis) population at Mt Etna. A thermal camera and automated counting system were trialled to see if the technology would work at Mt Etna with the goal of producing accurate counts of bats. Although the heat radiating from the rock meant that the background at Mt Etna was not perfect, the automated system was able to recognise and count the bats as they emerged. The number of bats emerging from the cave varied from 82 677 in 2017/18 through to 139 783 in 2018/19. These estimates are within the range of those made in the 1960 to 1980s, which suggests that the population is stable, but we are unsure of the accuracy of these previous counts. These results demonstrate that it is possible to obtain accurate counts of reasonably large bat populations such as Bat Cleft and the methodology may have application for other gregarious and nocturnal species whose numbers can be difficult to count at night.

Tatvan, Ahlat ve Adilcevaz Bölgelerinde Sahil Kumlarında Radon Aktivite Konsantrasyon Seviyelerinin Belirlenmesi

Radon gas (222Rn) is an important parameter in natural radioactivity studies because it constitutes the majority of natural radioactivity and is the second most important cause of lung cancer in terms of human health. In this study, radon gas activity concentration levels were determined in beach sands of Tatvan, Ahlat and Adilcevaz regions where are on the shore of Lake Van. Beach sand samples were collected from 15 predetermined points on the coastline of Lake Van, which was formed by the effect of Nemrut and Süphan extinct volcanoes. After the collected samples were dried and sieved, they were placed in a container with the CR-39 passive solid-state track detector (SSNTD - Solid State Nuclear Tracking Detector). The detectors exposed to the radon gas emitted from the beach sands for 61 days were removed and subjected to chemical etching at 90 ° C in 6.25 M NaOH solution for 4 and a half hours. Radon gas traces were counted on the etched detectors using the RadoSYS automatic counting system. Radon concentration levels were determined using the counting results in terms of trace density. The maximum and the minumum values were obtained as 459.42 ± 7.41 Bq.m-3 and 105.34 ± 3.09 Bq.m-3 in the ADCZ-6 sample and TTVN-12 sample, respectively. The average activity value was determined as 193.30 Bq.m-3. When evaluated regionally, the highest activity value was determined as an average of 240.96 Bq.m-3 in Tatvan, then 209.57 Bq.m-3 in Ahlat and 129.36 Bq.m-3 in Adilcevaz. Using the activity values obtained, the contour distribution map was obtained by the Kriging method. Furthermore, annual effective dose values were determined for sand samples. The largest and smallest dose values were found to be 11.57 mSv.y-1 and 2.65 mSv.y-1, respectively. The obtained results were compared with limit values and radon gas levels of beach sands in the other different areas of Turkey and other countries.

Developing of automatic lap counting system for electric vehicle at Shell Eco-marathon competition

The article shows the development of a closed circuit track counting system made from a prototype electric car that was specifically developed for the Shell Eco Marathon. The system records the laps made by transmitting and receiving radio waves (RF). The entire electronic system was built on the Arduino microcontroller platform and one-way radio frequency transceiver modules. The design of the system was developed using Catia V5 3D modeling software. Additive technology was used to reduce the mass of the system.

Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data.

Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd’s traffic state. Incorporating multiple functionally complementary sensor types is expensive. CMSs are needed that exploit data fusion opportunities to limit the number of (more expensive) sensors. This research estimates a data fusion algorithm to enhance the functionality of a CMS featuring Wi-Fi sensors by means of a small number of automated counting systems. Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best.

Dego: A Novel Variable to Predict the Live-Birth Rate in Patients with Intrauterine Adhesions Following Hysteroscopic Adhesiolysis

Study Objective To propose the density of endometrial glandular openings (DEGO) as a simple and reliable new variable in predicting live birth after hysteroscopic adhesiolysis (HA). Design Retrospective cohort study. Setting University hospital. Patients or Participants 469 patients with intra-uterine adhesions who underwent hysteroscopic adhesiolysis followed by second look hysteroscopy with images recorded. 12 were lost to follow-up or excluded by criteria. The remaining 457 were included in the study and followed up over a period of 2 years. 231 patients had live-births,82 patients had recurrent spontaneous abortion without live-birth and 144 patients remained infertile at the end of the study. Interventions None Measurements and Main Results N/A. Main Outcome Measure(s): The second look hysteroscopic images were evaluated by an automatic counting system to calculate the density of endometrial glandular openings (DEGO). The American Fertility Society (AFS) scores were also calculated for each patient. Logistic regression analysis was performed to develop a prediction model for each of these scores to predict live-birth-rate following hysteroscopic adhesiolysis. The performance of each of these prediction models was compared by calculating the area under the curve (AUC). Result(s): The logistic regression analysis revealed that both DEGO and AFS scores were closely correlated with live-birth rates in patients with intra-uterine adhesions (p=0). The AUCs of AFS scores pre-operatively and post-operatively (evaluated during the final second-look hysteroscopy) were 0.7611 and 0.7691 respectively; The AUC for DEGO post-operatively (evaluated during the final second-look hysteroscopy) was 0.8628. When compared with pre- or post-operative AFS score, the post-operative DEGO had an optimal prediction performance (p=0.0002, p Conclusion Post-operative DEGO has superior performance to AFS score in predicting the live-birth rate following hysteroscopic adhesiolysis for patients with intrauterine adhesions.

Accelerometer-Based Automated Counting of Ten Exercises without Exercise-Specific Training or Tuning.

Measuring physical activity using wearable sensors is essential for quantifying adherence to exercise regiments in clinical research and motivating individuals to continue exercising. An important aspect of wearable activity tracking is counting particular movements. One limitation of many previous models is the need to design the counting for a specific exercise. However, during physical therapy, some movements are unique to the patient and also valuable to track. To address this, we create an automatic repetition counting system that is flexible enough to measure multiple distinct and repeating movements during physical therapy without being trained on the specific motion. Accelerometers, using smartphones, were attached to the body or held by participants to track repetitive motions during different exercises. 18 participants completed a series of 10 exercises for 30 seconds, including arm circles, bicep curls, bridges, sit-ups, elbow extensions, leg lifts, lunges, push-ups, squats, and upper trunk rotations. To count the repetitions of each exercise, we apply three analysis techniques: (a) threshold crossing, (b) threshold crossing with a low-pass filter, and (c) Fourier transform. The results demonstrate that arm circles and push-ups can be tracked well, while less periodic and irregular motions such as upper trunk rotations are more difficult. Overall, threshold crossing with low-pass filtering achieves the best performance among these methods. We conclude that the proposed automatic counting system is capable of tracking exercise repetition without prior training and development for that activity.

Automatic mantispid egg detection and counting using image nature

Mantispids are small brown bugs about 1.5 cm in length. Mantispid eggs are produced in large quantities, with about 1000 eggs per spawning, and are tiny and densely packed. Traditionally, mantispid eggs are counted manually. However, counting such a large quantity of eggs is difficult. To provide accurate data for researchers, we detail methods to accurately detect and count the number of mantispid eggs using image processing. The following methods were used to count the mantispid eggs: background estimation, morphological image processing, background subtraction, stretching, image thresholding, gray-level transformation, labeling and counting. The results of automated counting were compared with the results of manual counting. The segmentation results were verified, and the accuracy of the mantispid egg counts was determined to be 100%. This provides a useful resource for mantispid egg counting. The automatic counting system cannot only count mantispid eggs, it can also be used to count other similar insect eggs.

Diagnostic performance of McMaster, Wisconsin, and automated egg counting techniques for enumeration of equine strongyle eggs in fecal samples

Fecal egg counts are the cornerstone of equine parasite control programs. Previous work led to the development of an automated, image-analysis-based parasite egg counting system. The system has been further developed to include an automated reagent dispenser unit and a custom camera (CC) unit that generates higher resolution images, as well as a particle shape analysis (PSA) algorithm and machine learning (ML) algorithm. The first aim of this study was to conduct a comprehensive comparison of method precision between the original smartphone (SP) unit with the PSA algorithm, CC/PSA, CC/ML, and the traditional McMaster (MM) and Wisconsin (MW) manual techniques. Additionally, a Bayesian analysis was performed to estimate and compare sensitivity and specificity of all five methods. Feces were collected from horses, screened with triplicate Mini-FLOTAC counts, and placed into five categories: negative (no eggs seen), > 0 - ≤ 200 eggs per gram (EPG), > 200 - ≤ 500 EPG, > 500 - ≤ 1000 EPG, and > 1000 EPG. Ten replicates per horse were analyzed for each technique. Technical variability for samples > 200 EPG was significantly higher for MM than CC/PSA and CC/ML (p < 0.0001). Biological variability for samples> 0 was numerically highest for CC/PSA, but with samples > 200 EPG, MM had a significantly lower CV than MW (p = 0.001), MW had a significantly lower CV than CC/PSA (p < 0.0001), CC/ML had a significantly lower CV than both MW and SP/PSA (p < 0.0001, p = 0.0003), and CC/PSA had a significantly lower CV than CC/SP (p = 0.0115). Sensitivity was> 98 % for all five methods with no significant differences. Specificity, however, was significantly the highest for CC/PSA, followed numerically by SP/PSA, MM, CC/ML, and finally MW. Overall, the automated counting system is a promising new development in equine parasitology. Continued refinement to the counting algorithms will help improve precision and specificity, while additional research in areas such as egg loss, analyst variability at the counting step, and accuracy will help create a complete picture of its impact as a new fecal egg count method.