The Experts below are selected from a list of 338238 Experts worldwide ranked by ideXlab platform
Gang Logan Liu - One of the best experts on this subject based on the ideXlab platform.
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white blood Cell Counting on smartphone paper electrochemical sensor
Biosensors and Bioelectronics, 2017Co-Authors: Xinhao Wang, Guohong Lin, Guangzhe Cui, Xiangfei Zhou, Gang Logan LiuAbstract:White blood Cell (WBC) analysis provides rich information in rapid diagnosis of acute bacterial and viral infections as well as chronic disease management. For patients with immune deficiency or leukemia WBC should be persistently monitored. Current WBC Counting method relies on bulky instrument and trained personnel and is time consuming. Rapid, low-cost and portable solution is in highly demand for point of care test. Here we demonstrate a label-free smartphone based electrochemical WBC Counting device on microporous paper with patterned gold microelectrodes. WBC separated from whole blood was trapped by the paper with microelectrodes. WBC trapped on the paper leads to the ion diffusion blockage on microelectrodes, therefore Cell concentration is determined by peak current on the microelectrodes measured by a differential pulse voltammeter and the quantitative results are collected by a smartphone wirelessly within 1min. We are able to rapidly quantify WBC concentrations covering the common physiological and pathological range (200-20000μL-1) with only 10μL sample and high repeatability as low as 10% in CoV (Coefficient of Variation). The unique smartphone paper electrochemical sensor ensures fast Cell quantification to achieve rapid and low-cost WBC analysis at the point-of-care under resource limited conditions.
Mita Nasipuri - One of the best experts on this subject based on the ideXlab platform.
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blood smear analyzer for white blood Cell Counting
Applied Soft Computing, 2016Co-Authors: Pramit Ghosh, Debotosh Bhattacharjee, Mita NasipuriAbstract:Graphical abstractDisplay Omitted Total count and differential count of leukocytes or white blood Cells (WBC) in blood samples are very important pathological factors for diagnosing a disease. There are not enough pathological infrastructures in the remote places of India and other developing countries. The objective of this work is to design a system, compatible with telemedicine, for automatic calculation of the total count and differential count of WBC from the blood smear slides. Hemocytometer based WBC Counting provides more accurate result than manual Counting, but hemocytometer preparation process needs expertise. As this device is targeted for remote places, blood smear technique is adopted to reduce the overhead of the operator. In the proposed system, microscopic images of blood smear sample are processed to highlight the WBC for segmentation. Region segmentation procedure involves background scaling and redundant region elimination from the region set. After segmentation, the more accurate region boundary is restored by using gradient based region growing with neighbourhood influence. Individual regions are separately classified on the basis of shape, size, color and texture features independently using different fuzzy and non-fuzzy techniques. A final decision is taken by combining these classification results, which is a kind of hybridization. A set of rules has been generated for making final classification decision based on outputs from various classifiers. The sensitivity and specificity of the system are found to be 96.4% and 79.6%, respectively on a database of 150 blood smear slides collected from different health centres of Kolkata Municipal Corporation, Kolkata, India.
Patrick R Hof - One of the best experts on this subject based on the ideXlab platform.
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current automated 3d Cell detection methods are not a suitable replacement for manual stereologic Cell Counting
Frontiers in Neuroanatomy, 2014Co-Authors: Christoph Schmitz, Brian S Eastwood, Susan Tappan, Jacob R Glaser, Daniel A Peterson, Patrick R HofAbstract:Stereologic Cell Counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic Cell Counting is that the user must manually decide whether or not each Cell is counted according to three-dimensional (3D) stereologic Counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic Cell Counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “Cell Counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated Cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic Cell Counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of Cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D Cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38–99% and false-positive rates from 3.6–82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of Cell numbers. Thus, at present, stereologic Cell Counting with manual decision for object inclusion according to unbiased stereologic Counting rules remains the only adequate method for unbiased Cell quantification in histologic tissue sections.
Gilbert Bigras - One of the best experts on this subject based on the ideXlab platform.
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Cell Counting by regression using convolutional neural network
European Conference on Computer Vision, 2016Co-Authors: Yao Xue, Nilanjan Ray, Judith Hugh, Gilbert BigrasAbstract:The ability to accurately quantitate specific populations of Cells is important for precision diagnostics in laboratory medicine. For example, the quantization of positive tumor Cells can be used clinically to determine the need for chemotherapy in a cancer patient. In this paper, we describe a supervised learning framework with Convolutional Neural Network (CNN) and cast the Cell Counting task as a regression problem, where the global Cell count is taken as the annotation to supervise training, instead of following the classification or detection framework. To further decrease the prediction error of Counting, we tune several cutting-edge CNN architectures (e.g. Deep Residual Network) into the regression model. As the final output, not only the Cell count is estimated for an image, but also its spatial density map is provided. The proposed method is evaluated with three state-of-the-art approaches on three Cell image datasets and obtain superior performance.
Pramit Ghosh - One of the best experts on this subject based on the ideXlab platform.
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blood smear analyzer for white blood Cell Counting
Applied Soft Computing, 2016Co-Authors: Pramit Ghosh, Debotosh Bhattacharjee, Mita NasipuriAbstract:Graphical abstractDisplay Omitted Total count and differential count of leukocytes or white blood Cells (WBC) in blood samples are very important pathological factors for diagnosing a disease. There are not enough pathological infrastructures in the remote places of India and other developing countries. The objective of this work is to design a system, compatible with telemedicine, for automatic calculation of the total count and differential count of WBC from the blood smear slides. Hemocytometer based WBC Counting provides more accurate result than manual Counting, but hemocytometer preparation process needs expertise. As this device is targeted for remote places, blood smear technique is adopted to reduce the overhead of the operator. In the proposed system, microscopic images of blood smear sample are processed to highlight the WBC for segmentation. Region segmentation procedure involves background scaling and redundant region elimination from the region set. After segmentation, the more accurate region boundary is restored by using gradient based region growing with neighbourhood influence. Individual regions are separately classified on the basis of shape, size, color and texture features independently using different fuzzy and non-fuzzy techniques. A final decision is taken by combining these classification results, which is a kind of hybridization. A set of rules has been generated for making final classification decision based on outputs from various classifiers. The sensitivity and specificity of the system are found to be 96.4% and 79.6%, respectively on a database of 150 blood smear slides collected from different health centres of Kolkata Municipal Corporation, Kolkata, India.