Computer-Aided Somatic Cells Counting on Three Different Milk Staining Condition

The maximum amount of somatic cell in a millilitre of milk defined by National Standardization Agency is 4x105 cells/ml. The condition of milk will be excluded from a decent quality if the amount of somatic cell is greater than it. Microscopic image of somatic cells counting is a conventional method which utilized by farmer or researcher to count the number of somatic cells from dairy milk sample. The problems of this method are: 1) The counting process is manually conducted which prone to miscalculation, 2) The different colour of staining (purplish, bluish, and greenish) may complicate the calculation. In this research, a computer-based approach is proposed to calculate the number of somatic cells from sample of milk and to eliminate the difficulty of different colour due to staining technique. Image processing knowledge i.e. erosion, dilation, colour conversion, and BLOB Analysis are involved and utilized to achieve the objective. Overall, the correctness of the system in detecting the number of somatic cells is 94%. From this study, a comprehensive system to calculate the number of somatic cells from cow’s milk will be closer to be implemented

Classification and recognition of milk somatic cell images based on PolyLoss and PCAM-Reset50

Somatic cell count (SCC) is a fundamental approach for determining the quality of cattle and bovine milk. So far, different classification and recognition methods have been proposed, all with certain limitations. In this study, we introduced a new deep learning tool, i.e., an improved ResNet50 model constructed based on the residual network and fused with the position attention module and channel attention module to extract the feature information more effectively. In this paper, macrophages, lymphocytes, epithelial cells, and neutrophils were assessed. An image dataset for milk somatic cells was constructed by preprocessing to increase the diversity of samples. PolyLoss was selected as the loss function to solve the unbalanced category samples and difficult sample mining. The Adam optimization algorithm was used to update the gradient, while Warm-up was used to warm up the learning rate to alleviate the overfitting caused by small sample data sets and improve the model's generalization ability. The experimental results showed that the classification accuracy, precision rate, recall rate, and comprehensive evaluation index F value of the proposed model reached 97%, 94.5%, 90.75%, and 92.25%, respectively, indicating that the proposed model could effectively classify the milk somatic cell images, showing a better classification performance than five previous models (i.e., ResNet50, ResNet18, ResNet34, AlexNet andMobileNetv2). The accuracies of the ResNet18, ResNet34, ResNet50, AlexNet, MobileNetv2, and the new model were 95%, 93%, 93%, 56%, 37%, and 97%, respectively. In addition, the comprehensive evaluation index F1 showed the best effect, fully verifying the effectiveness of the proposed method in this paper. The proposed method overcame the limitations of image preprocessing and manual feature extraction by traditional machine learning methods and the limitations of manual feature selection, improving the classification accuracy and showing a strong generalization ability

First Evaluation of Infrared Thermography as a Tool for the Monitoring of Udder Health Status in Farms of Dairy Cows

The aim of the present study was to test infrared thermography (IRT), under field conditions, as a possible tool for the evaluation of cow udder health status. Thermographic images (n. 310) from different farms (n. 3) were collected and evaluated using a dedicated software application to calculate automatically and in a standardized way, thermographic indices of each udder. Results obtained have confirmed a significant relationship between udder surface skin temperature (USST) and classes of somatic cell count in collected milk samples. Sensitivity and specificity in the classification of udder health were: 78.6% and 77.9%, respectively, considering a level of somatic cell count (SCC) of 200,000 cells/mL as a threshold to classify a subclinical mastitis or 71.4% and 71.6%, respectively when a threshold of 400,000 cells/mL was adopted. Even though the sensitivity and specificity were lower than in other published papers dealing with non-automated analysis of IRT images, they were considered acceptable as a first field application of this new and developing technology. Future research will permit further improvements in the use of IRT, at farm level. Such improvements could be attained through further image processing and enhancement, and the application of indicators developed and tested in the present study with the purpose of developing a monitoring system for the automatic and early detection of mastitis in individual animals on commercial farms

J Dairy Sci

Removal of teat-end debris is one of the most critical steps in the premilking process. We aimed to estimate inter- and intra-rater reliability of an observation-based rating scale of dairy parlor worker teat-cleaning performance. A nonrandom sample of 8 experienced raters provided teat swab debris ratings scored on a 4-point ordinal visual scale for 175 teat swab images taken immediately after teat cleaning and before milking unit attachment. To overcome the uncertainty associated with visual inspection and observation-based rating scales, we assessed the reliability of an automated observer-independent method to assess teat-end debris using digital image processing and machine learning techniques to quantify the type and amount of debris material present on each teat swab image. Cohen's kappa coefficient (\u3ba) was used to assess inter-rater score agreement on 175 teat swab images, and the intraclass correlation coefficient was used to assess both intra-rater score agreement and machine reliability. The reliability of debris scoring of teat swabs by raters was low (overall \u3ba = 0.43), whereas the machine-based rating system demonstrated near-perfect reliability (Pearson r > 0.99). Our findings suggest that machine-based rating systems of worker performance are much more reliable than observational-based methods when evaluating premilking teat cleanliness. Teat swab image analysis technology can be further developed for training and quality control purposes to enable more efficient, reliable, and independent feedback on worker milking performance. As automated technologies are becoming more popular on dairy farms, machine-based teat cleanliness scoring could also be incorporated into automated milking systems.T42OH008421/ACL/ACL HHS/United StatesU54 OH008085/OH/NIOSH CDC HHS/United States2020-01-02T00:00:00Z31178196PMC69393137040vault:3435

New Insights into Cell Encapsulation and the Role of Proteins During Flow Cytometry

peer-reviewedModern approaches to science tend to follow divergent paths. On one hand, instruments and technologies are developed to capture as much information as possible with the need for complex data analysis to identify problematic issues. On the other hand, formulation focused, minimalistic approaches that gather only the most pertinent data for specific questions also represent a powerful methodology. This chapter will provide many examples of the latter by integrating Flow Cytometry (FACS - Fluorescence-Activated Cell Sorting) technology with high throughput screening (HTS) of encapsulation systems with extensive utility of one-dimensional (1-D) imaging for protein localisation. In this regard, less information is acquired from each cell, data files will be more manageable, easier to analyse and throughput screening will be significantly enhanced beyond traditional HTS analysis, irrespective of the protein concentration present in the background or delivery media.Some of the cytometric work presented in this chapter was supported by the Irish Dairy Research Trust project NU518 “Probiotic Protection”, the Irish National Development Plan 2007 to 2013 and Science Foundation Ireland (SFI)

Precision technologies to address dairy cattle welfare: focus on lameness, mastitis and body condition

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and realtime assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.FE1B-06B2-126F | Jos? Pedro Pinto de Ara?joN/

Technologies used at advanced dairy farms for optimizing the performance of dairy animals: A review

Superior germplasm, better nutrition strategies, health care facilities and improved dairy husbandry practices have boosted milk yield and its quality with a rapid rate. Per cow productivity has risen up sharply with considerable increase in the population of dairy animals. Recent era has witnessed the extension of large dairy farms around the world. Demand for high quality and increased quantity of milk is of the prime concern for all the dairy farms. With an increase in the size of animals in a farm, the labour requirement also rises up. Availability of skilled labour at low wage rate is becoming difficult. In last couple of decades, the cost of microprocessors has been reduced to an affordable level. The economic availability of engineered processors, artificial intelligence, improved data statistics combined with expert suggestions has created a revolution in livestock farming. Advanced engineered devices have become alternative to reduce high labour cost. This review focuses on latest knowledge and emerging developments in animal’s welfare focused biomarker activities and activity-based welfare assessment like oestrus, lameness and others. Use of enhanced sensors and data technologies with expert based solutions is anticipated to bring out a substantial improvement in existing dairy farming practices

Developing quantitative image analysis pipelines for scoring histological panoramic images : Testing Rab24 as a possible biomarker for cancer

Biomarkers are highly essential to improve diagnosis, confirm the diseases' development, and monitor the treatment. Biomarker discovery requires analysis of a large quantity of data which is aided by computational tools. One of the methods widely used in the search of new biomarkers is immunohistochemistry of tissue samples. Numerous tools are available to detect different cell types in tissues in histological sections; still, the need for more advanced and quantitative analysis is growing. The most successful paradigms to meet these novel needs are using deep learning-based networks. Rab24, an atypical member of the Rab protein family, plays a role in the late steps of endosomal degradation, in mitochondrial plasticity, and in the clearance of autolysosomes in basal autophagy. Rab24 has been connected to neurodegeneration and cancer. It has been shown to be overexpressed in hepatocellular carcinoma (HCC) and to enhance HCC's malignant phenotype. These findings together indicate that Rab24 might be a potential biomarker for cancer, and its modulation might be used as a strategy for cancer therapy. This project was undertaken to investigate the expression of Rab24 in different types of human cancers. Rab24 was detected by immunohistochemical staining in cancer tissue samples embedded in paraffin. For the evaluation of expression levels, detailed image analysis pipelines were developed to combine an open-source software called QuPath with a deep learning network, StarDist, in order to setup a robust quantitative cell detection compatible with histological panoramic images. Based on our current analysis, 5 cancer types, including angiosarcoma, stomach gastrointestinal stromal tumor (GIST), rectal neuroendocrine carcinoma (NEC), liposarcoma and fibrosarcoma were selected as potential candidates for further investigation

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions