scholarly journals Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarada M. W. Lee ◽  
Andrew Shaw ◽  
Jodie L. Simpson ◽  
David Uminsky ◽  
Luke W. Garratt
Keyword(s):  
Ground Truth ◽  
Differential Count ◽  
Single Centre ◽  
Ground Truth Data ◽  
Differential Cell Count ◽  
Cell Counts ◽  
Bounding Box ◽  
Cell Class ◽  
Differential Cell ◽  
Class Segmentation

AbstractDifferential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered. We present differential count network (“DCNet”), an annotation efficient modality that utilises keypoint detection to locate in brightfield images the centre points of cells (not nuclei) and their cell class. The single centre point annotation for DCNet lowered burden for experts to generate ground truth data by 77.1% compared to bounding box labeling. Yet centre point annotation still enabled high accuracy when training DCNet on a multi-class algorithm on whole cell features, matching human experts in all 5 object classes in average precision and outperforming humans in consistency. The efficacy and efficiency of the DCNet end-to-end system represents a significant progress toward an open source, fully computationally approach to differential cell count based diagnosis that can be adapted to any pathology need.

Cell function in the bovine mammary gland: a preliminary study on interdependence of healthy and infected udder quarters

2007 ◽  
Vol 74 (2) ◽  
pp. 174-179 ◽  
Author(s):  
Roswitha Merle ◽  
Anke Schröder ◽  
Jörn Hamann
Keyword(s):  
Cell Count ◽  
Cell Function ◽  
Cell Activity ◽  
Polymorphonuclear Leucocytes ◽  
Differential Cell Count ◽  
Immune Reactions ◽  
Cell Counts ◽  
Differential Cell ◽  
Group A ◽  
Group B

Udder defence mechanisms are not completely explained by current mastitis research. The anatomical construction of the udder implies that infection of one udder quarter does not influence the immune status of neighbouring quarters. To test this hypothesis, we compared the immune reactions of individual udder quarters in response to microbial attacks. In the course of immune reactions, polymorphonuclear leucocytes (PMN) release oxygen radicals, which can be determined by chemiluminescence (CL). Milk from 140 udder quarters of 36 cows was analysed for somatic cell count (SCC), differential cell count, viability and CL activity. Quarters with an SCC <100000 cells/ml and free of pathogens were defined as uninfected, all other quarters were categorized as infected. Three groups of cows were classified cytologically: group A (healthy, 11 animals, SCC limit <100000 cells/ml); group B (moderate mastitis, 8 cows, SCC [ges ]100000 and <400000 cells/ml in at least one quarter); and group C (severe mastitis, 17 cows, SCC [ges ]400000 cells/ml in at least one quarter). Infected and uninfected quarters in groups B and C were analysed separately. Viability of PMN leucocytes was significantly (P=0·0012) lower in group A (72·6%) than in healthy quarters of group C (84·0%). Lowering the SCC limit of healthy quarters to <50000 cells/ml (group A: all quarters within the udder) revealed striking differences between samples of groups B and C: in addition to varying differential cell counts and viabilities, CL activity of group B<50 (2929 CL units/million PMN) was markedly lower than that of the other groups (5616 in group A<50 and 6445 CL units/million PMN in group C<50). These results allow the conclusion that the infection of one udder quarter influences the cell activity of neighbouring quarters. When the SCC threshold for healthy quarters was reduced to 50000 cells/ml, greater differences in cell activities were detected between healthy udders and healthy quarters of infected udders.

scholarly journals The Efficiency of Sputum Cell Counts in Cystic Fibrosis

2007 ◽  
Vol 14 (2) ◽  
pp. 99-103 ◽  
Author(s):  
Lata Jayaram ◽  
N Renee Labiris ◽  
Ann Efthimiadis ◽  
Helen Vlachos-Mayer ◽  
Frederick E Hargreave ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Cell Count ◽  
Sputum Sample ◽  
Reliability And Validity ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Differential Cell Count ◽  
Cell Counts ◽  
Differential Cell ◽  
The Mean

BACKGROUND: Technical factors relating to processing viscid sputum in cystic fibrosis (CF) and their influence on the reproducibility and validity of cell counts need to be evaluated. In addition, the methods need to be standardized so that they can be applied clinically and in research.OBJECTIVE: To examine the efficiency, reliability and validity of processing small volumes of spontaneously expectorated sputum from subjects with CF.METHODS: Sputum was collected from adults with CF (n=35) and compared with sputum from adults with infective bronchitis or bronchiectasis (IB/B) (n=16), or with asthma or chronic obstructive pulmonary disease (AS/COPD) (n=25). Selected sputum (100 mg to 200 mg) was processed with dithiothreitol (0.1%) and filtered. Total cell count (TCC) and viability were obtained in a counting chamber and cytospins were prepared and stained with Wright’s for a differential cell count. Sputum and filter remnant were processed for TCC, viability and differential cell count, and the efficiency was determined by comparing the mean loss in cell yield to the filter. Two different portions from the same sputum sample were processed for cell counts to determine reproducibility. Results were compared with those from IB/B and AS/COPD groups.RESULTS: Efficiency of cell dispersal was excellent and similar to that in AS/COPD and IB/B groups. Reproducibility of cell counts from two portions of a sputum sample was high (R≥0.80). CF sputum demonstrated a raised TCC and neutrophilia similar to IB/B but significantly higher than AS/COPD.CONCLUSION: The selection method of evaluating cell counts in viscid CF sputum is efficient, reproducible and valid.

The influence of technical factors on differential cell count in milk

2005 ◽  
Vol 72 (2) ◽  
pp. 153-158 ◽  
Author(s):  
Anke C Schröder ◽  
Jörn Hamann
Keyword(s):  
Cell Count ◽  
Flow Cytometric Analysis ◽  
Polymorphonuclear Neutrophils ◽  
Cover Glass ◽  
Differential Cell Count ◽  
Macrophage Population ◽  
Udder Health ◽  
Cell Counts ◽  
Differential Cell ◽  
The Impact

Differential cell count of milk is a traditional parameter for the evaluation of udder health. The literature shows great variation in differential cell counts of the milk of healthy mammary glands: macrophages range from 0% to 80%, lymphocytes from 1·5% to 79·5%, polymorphonuclear neutrophils from 3% to 95%, and epithelial cells from 1% to 19%. We conducted three studies to seek explanations for such variation. In the first, we evaluated the impact of polyethylene and glass sampling bottles. The aim of the second study was to compare the results of differential cell counts performed by three different technicians. The third study evaluated two methods of smear preparation. When polyethylene plastic bottles were used, the macrophage population was minimized but lymphocytes remained unaffected. This was shown by an exemplary flow cytometric analysis using four monoclonal antibodies against three lymphocyte surface structures. There were significant differences in the differential cell counts of 40 smears made by three technicians despite identical operating procedures. For the sediment smear, milk was centrifuged once and the sediment spread by eye on a glass slide. For the “coffee grinder” smear method, the sample was subjected to four centrifugations and then placed on a cover glass in order to spread the sediment using centrifugal force. The coffee grinder procedure led to a reduction of lymphocytes and an enrichment of polymorphonuclear neutrophils without affecting the macrophage population. Both methods made it possible to distinguish different udder health classes. It can be concluded that differential cell counts are a useful tool for comparing and monitoring udder health only if: samples are taken in a glass bottle; smears are prepared with the identical technique; and the differential cell counts are performed by a single person.

scholarly journals Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study

2020 ◽  
Vol 44 (10) ◽  
Author(s):  
Hong Jin ◽  
Xinyan Fu ◽  
Xinyi Cao ◽  
Mingxia Sun ◽  
Xiaofen Wang ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Cell Count ◽  
Marrow Cell ◽  
Differential Count ◽  
Cell Classification ◽  
Initial Development ◽  
Differential Cell Count ◽  
Analysis Methods ◽  
Differential Cell ◽  
Cell Series

Abstract Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8–90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

scholarly journals Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

2020 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Wen-Hao Su ◽  
Jiajing Zhang ◽  
Ce Yang ◽  
Rae Page ◽  
Tamas Szinyei ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Ground Truth ◽  
Wheat Breeding ◽  
Head Blight ◽  
Detection Rates ◽  
Ground Truth Data ◽  
Resistant Cultivars ◽  
Feature Pyramid ◽  
Rater Error ◽  
Wheat Lines

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

scholarly journals Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4050
Author(s):  
Dejan Pavlovic ◽  
Christopher Davison ◽  
Andrew Hamilton ◽  
Oskar Marko ◽  
Robert Atkinson ◽  
...  
Keyword(s):  
Neural Network ◽  
Model Performance ◽  
Ground Truth ◽  
Practical Implementation ◽  
Ground Truth Data ◽  
Battery Lifetime ◽  
Implementation Challenges ◽  
Memory Footprint ◽  
Commercial Farms ◽  
Using Data

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity profiles are now used extensively on commercial farms and have evolved to identify behaviours such as the time spent ruminating and eating at an individual animal level. Acquiring this information at scale is central to informing on-farm management decisions. The paper presents the development of a Convolutional Neural Network (CNN) that classifies cattle behavioural states (`rumination’, `eating’ and `other’) using data generated from neck-mounted accelerometer collars. During three farm trials in the United Kingdom (Easter Howgate Farm, Edinburgh, UK), 18 steers were monitored to provide raw acceleration measurements, with ground truth data provided by muzzle-mounted pressure sensor halters. A range of neural network architectures are explored and rigorous hyper-parameter searches are performed to optimise the network. The computational complexity and memory footprint of CNN models are not readily compatible with deployment on low-power processors which are both memory and energy constrained. Thus, progressive reductions of the CNN were executed with minimal loss of performance in order to address the practical implementation challenges, defining the trade-off between model performance versus computation complexity and memory footprint to permit deployment on micro-controller architectures. The proposed methodology achieves a compression of 14.30 compared to the unpruned architecture but is nevertheless able to accurately classify cattle behaviours with an overall F1 score of 0.82 for both FP32 and FP16 precision while achieving a reasonable battery lifetime in excess of 5.7 years.

A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

2021 ◽  
pp. 0021955X2110210
Author(s):  
Alejandro E Rodríguez-Sánchez ◽  
Héctor Plascencia-Mora
Keyword(s):  
Neural Network ◽  
Energy Absorption ◽  
Mechanical Energy ◽  
Compressive Loading ◽  
Ground Truth ◽  
Expanded Polystyrene ◽  
Polystyrene Foam ◽  
Stress Strain ◽  
Ground Truth Data ◽  
Expanded Polystyrene Foam

Traditional modeling of mechanical energy absorption due to compressive loadings in expanded polystyrene foams involves mathematical descriptions that are derived from stress/strain continuum mechanics models. Nevertheless, most of those models are either constrained using the strain as the only variable to work at large deformation regimes and usually neglect important parameters for energy absorption properties such as the material density or the rate of the applying load. This work presents a neural-network-based approach that produces models that are capable to map the compressive stress response and energy absorption parameters of an expanded polystyrene foam by considering its deformation, compressive loading rates, and different densities. The models are trained with ground-truth data obtained in compressive tests. Two methods to select neural network architectures are also presented, one of which is based on a Design of Experiments strategy. The results show that it is possible to obtain a single artificial neural networks model that can abstract stress and energy absorption solution spaces for the conditions studied in the material. Additionally, such a model is compared with a phenomenological model, and the results show than the neural network model outperforms it in terms of prediction capabilities, since errors around 2% of experimental data were obtained. In this sense, it is demonstrated that by following the presented approach is possible to obtain a model capable to reproduce compressive polystyrene foam stress/strain data, and consequently, to simulate its energy absorption parameters.

scholarly journals Multi-Temporal Arable Land Monitoring in Arid Region of Northwest China Using a New Extraction Index

2021 ◽  
Vol 13 (9) ◽  
pp. 5274
Author(s):  
Xinyang Yu ◽  
Younggu Her ◽  
Xicun Zhu ◽  
Changhe Lu ◽  
Xuefei Li
Keyword(s):  
Arable Land ◽  
Ground Truth ◽  
Northwest China ◽  
Hexi Corridor ◽  
Ground Truth Data ◽  
Land Protection ◽  
Promising Tool ◽  
Study Results ◽  
Multi Temporal ◽  
The Mean

Development of a high-accuracy method to extract arable land using effective data sources is crucial to detect and monitor arable land dynamics, servicing land protection and sustainable development. In this study, a new arable land extraction index (ALEI) based on spectral analysis was proposed, examined by ground truth data, and then applied to the Hexi Corridor in northwest China. The arable land and its change patterns during 1990–2020 were extracted and identified using 40 Landsat TM/OLI images acquired in 1990, 2000, 2010, and 2020. The results demonstrated that the proposed method can distinguish arable land areas accurately, with the User’s (Producer’s) accuracy and overall accuracy (kappa coefficient) exceeding 0.90 (0.88) and 0.89 (0.87), respectively. The mean relative error calculated using field survey data obtained in 2012 and 2020 was 0.169 and 0.191, respectively, indicating the feasibility of the ALEI method in arable land extracting. The study found that arable land area in the Hexi Corridor was 13217.58 km2 in 2020, significantly increased by 25.33% compared to that in 1990. At 10-year intervals, the arable land experienced different change patterns. The study results indicate that ALEI index is a promising tool used to effectively extract arable land in the arid area.

scholarly journals Detection of heat pumps from smart meter and open data

2020 ◽  
Vol 3 (S1) ◽  
Author(s):  
Andreas Weigert ◽  
Konstantin Hopf ◽  
Nicolai Weinig ◽  
Thorsten Staake
Keyword(s):  
Heat Pump ◽  
Open Data ◽  
Ground Truth ◽  
Heat Pumps ◽  
Geographical Information ◽  
Weather Data ◽  
Smart Meters ◽  
Ground Truth Data ◽  
Thermal Reservoir ◽  
Grid Operators

Abstract Heat pumps embody solutions that heat or cool buildings effectively and sustainably, with zero emissions at the place of installation. As they pose significant load on the power grid, knowledge on their existence is crucial for grid operators, e.g., to forecast load and to plan grid operation. Further details, like the thermal reservoir (ground or air source) or the age of a heat pump installation renders energy-related services possible that utility companies can offer in the future (e.g., detecting wrongly calibrated installations, household energy efficiency checks). This study investigates the prediction of heat pump installations, their thermal reservoir and age. For this, we obtained a dataset with 397 households in Switzerland, all equipped with smart meters, collected ground truth data on installed heat pumps and enriched this data with weather data and geographical information. Our investigation replicates the state of the art in the area of heat pump detection and goes beyond it, as we obtain three major findings: First, machine learning can detect the existence of heat pumps with an AUC performance metric of 0.82, their heat reservoir with an AUC of 0.86, and their age with an AUC of 0.73. Second, heat pump existence can be better detected using data during the heating period than during summer. Third the number of training samples to detect the existence of heat pumps must not be necessarily large in terms of the number of training instances and observation period.

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