Dataset Information

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

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?

SUBMITTER: Jin H

PROVIDER: S-EPMC7476995 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Jin Hong H Fu Xinyan X Cao Xinyi X Sun Mingxia M Wang Xiaofen X Zhong Yuhong Y Yang Suwen S Qi Chao C Peng Bo B He Xin X He Fei F Jiang Yongfang Y Gao Haiyan H Li Shun S Huang Zhen Z Li Qiang Q Fang Fengqi F Zhang Jun J

Journal of medical systems 20200907 10

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 Si ...[more]

PMID: 32894360

Similar Datasets

Project description:After fractionation of rabbit bone marrow into dividing (early) and non-dividing (late) erythroid cells, the adenylate cyclase activity of membrane ghosts was assayed in the presence of guanine nucleotides ((GTP and its analogue p[NH]ppG (guanosine 5'-[beta, gamma-imido]triphosphate))), the beta-adrenergic agonist L-isoprenaline (L-isoproterenol) and the antagonist L-propranolol. Both GTP and p[NH]ppG increased the adenylate cyclase activity of early and late erythroblasts, whereas the stimulating effect of the beta-adrenergic drug L-isoprenaline was limited to the immature dividing bone-marrow cells. The effect of L-isoprenaline was completely inhibited by the antagonist L-propranolol, confirming that the response was due to stimulation of beta-adrenergic receptors on the plasma membrane. The lack of response of non-dividing erythroblasts to beta-adrenergic stimuli is not due to loss of beta-receptors, since both dividing and non-dividing cells bind the selective ligand [125I]iodohydroxybenzylpindolol with almost equal affinities, the apparent dissociation constants, Kd, being 0.91 X 10(-8)M and 1.0 X 10(-8) M respectively. The number of beta-adrenergic receptors per cell was 2-fold higher in the dividing cells. No significant change in binding affinity for GTP and p[NH]ppG during erythroblast development was observed: the dissociation constants of both guanine nucleotides were almost identical with early and late erythroblast membrane preparations [2-3 (X 10(-7) M]. With dividing cells, however, in the presence of L-isoprenaline the dissociation constants of GTP and p[NH]ppG were lower (6 X 10(-8) M). The dose-response curves for isoprenaline competition in binding of [125I]iodohydroxybenzylpindolol by dividing cells showed that the EC50 (effective concentration for half maximum activity) value for isoprenaline was higher in the presence of p[NH]ppG. With non-dividing cells the EC50 value for isoprenaline was equal in the presence and in the absence of p[NH]ppG and similar to that observed with dividing-cell membranes in the presence of the nucleotide. Thus differentiation of rabbit bone-marrow erythroid cells seems to be accompanied by uncoupling of the beta-adrenergic receptors from the adenylate cyclase catalytic protein as well as by a decrease in the number of receptors per cell, but not by changes in the catecholamine and guanine-nucleotide-binding affinities.

Project description:BackgroundFreshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique to automatically detect, recognize, and identify algae genera from the divisions Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Literature shows that most automated analyses and identification of algae images were limited to only one type of algae. Automated identification system for tropical freshwater algae is even non-existent and this study is partly to fill this gap.ResultsThe development of the automated freshwater algae detection system involved image preprocessing, segmentation, feature extraction and classification by using Artificial neural networks (ANN). Image preprocessing was used to improve contrast and remove noise. Image segmentation using canny edge detection algorithm was then carried out on binary image to detect the algae and its boundaries. Feature extraction process was applied to extract specific feature parameters from algae image to obtain some shape and texture features of selected algae such as shape, area, perimeter, minor and major axes, and finally Fourier spectrum with principal component analysis (PCA) was applied to extract some of algae feature texture. Artificial neural network (ANN) is used to classify algae images based on the extracted features. Feed-forward multilayer perceptron network was initialized with back propagation error algorithm, and trained with extracted database features of algae image samples. System's accuracy rate was obtained by comparing the results between the manual and automated classifying methods. The developed system was able to identify 93 images of selected freshwater algae genera from a total of 100 tested images which yielded accuracy rate of 93%.ConclusionsThis study demonstrated application of automated algae recognition of five genera of freshwater algae. The result indicated that MLP is sufficient, and can be used for classification of freshwater algae. However for future studies, application of support vector machine (SVM) and radial basis function (RBF) should be considered for better classifying as the number of algae species studied increases.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure