scholarly journals Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1522
Author(s):  
Grzegorz Drałus ◽  
Damian Mazur ◽  
Anna Czmil
Keyword(s):  
Blood Cells ◽  
Complete Blood Count ◽  
White Blood Cells ◽  
Relevant Information ◽  
Disease Diagnosis ◽  
Cell Counting ◽  
Additional Advantage ◽  
Manual Counting ◽  
Detection And Counting ◽  
Deep Learning Network

A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.

Application of Biomedical Image Processing in Blood Cell Counting using Hough Transform

2016 ◽  
pp. 359-378 ◽  
Author(s):  
Manali Mukherjee ◽  
Kamarujjaman ◽  
Mausumi Maitra
Keyword(s):  
Blood Cell ◽  
Blood Cells ◽  
Complete Blood Count ◽  
White Blood Cells ◽  
Blood Stream ◽  
Cell Counting ◽  
Biomedical Image Processing ◽  
Microscopic Images ◽  
Biomedical Image ◽  
Rapid Heart Rate

In the field of biomedicine, blood cells are complex in nature. Nowadays, microscopic images are used in several laboratories for detecting cells or parasite by technician. The microscopic images of a blood stream contain RBCs, WBCs and Platelets. Blood cells are produced in the bone marrow and regularly released into circulation. Blood counts are monitored with a laboratory test called a Complete Blood Count (CBC). However, certain circumstances may cause to have fewer cells than is considered normal, a condition which is called “low blood counts”.This can be accomplished with the administration of blood cell growth factors. Common symptoms due to low red blood cells are:fatigue or tiredness, trouble breathing, rapid heart rate, difficulty staying warm, pale skin etc. Common symptoms due to low white blood cells are: infection, fever etc. It is important to monitor for low blood cell count because conditions could increase the risk of unpleasant and sometimes life-threatening side effects.

Application of Biomedical Image Processing in Blood Cell Counting Using Hough Transform

Biometrics ◽  
2017 ◽  
pp. 1175-1194
Author(s):  
Manali Mukherjee ◽  
Kamarujjaman ◽  
Mausumi Maitra
Keyword(s):  
Blood Cell ◽  
Blood Cells ◽  
Complete Blood Count ◽  
White Blood Cells ◽  
Blood Stream ◽  
Cell Counting ◽  
Biomedical Image Processing ◽  
Microscopic Images ◽  
Biomedical Image ◽  
Rapid Heart Rate

In the field of biomedicine, blood cells are complex in nature. Nowadays, microscopic images are used in several laboratories for detecting cells or parasite by technician. The microscopic images of a blood stream contain RBCs, WBCs and Platelets. Blood cells are produced in the bone marrow and regularly released into circulation. Blood counts are monitored with a laboratory test called a Complete Blood Count (CBC). However, certain circumstances may cause to have fewer cells than is considered normal, a condition which is called “low blood counts”. This can be accomplished with the administration of blood cell growth factors. Common symptoms due to low red blood cells are: fatigue or tiredness, trouble breathing, rapid heart rate, difficulty staying warm, pale skin etc. Common symptoms due to low white blood cells are: infection, fever etc. It is important to monitor for low blood cell count because conditions could increase the risk of unpleasant and sometimes life-threatening side effects.

scholarly journals Blood Cell Count Using Deep Learning Semantic Segmentation

2019 ◽  
Author(s):  
Thanh Tran ◽  
Lam Binh Minh ◽  
Suk-Hwan Lee ◽  
Ki-Ryong Kwon
Keyword(s):  
Deep Learning ◽  
Blood Cell ◽  
Cell Count ◽  
Blood Cells ◽  
Blood Smear ◽  
White Blood Cells ◽  
Semantic Segmentation ◽  
Peripheral Blood Smear ◽  
Cell Counting ◽  
Blood Cell Count

Clinically, knowing the number of red blood cells (RBCs) and white blood cells (WBCs) helps doctors to make the better decision on accurate diagnosis of numerous diseases. The manual cell counting is a very time-consuming and expensive process, and it depends on the experience of specialists. Therefore, a completely automatic method supporting cell counting is a viable solution for clinical laboratories. This paper proposes a novel blood cell counting procedure to address this challenge. The proposed method adopts SegNet - a deep learning semantic segmentation to simultaneously segment RBCs and WBCs. The global accuracy of the segmentation of WBCs, RBCs, and the background of peripheral blood smear images obtains 89% when segment WBCs and RBCs from the background of blood smear images. Moreover, an effective solution to separate grouped or overlapping cells and cell count is presented using Euclidean distance transform, local maxima, and connected component labeling. The counting result of the proposed procedure achieves an accuracy of 93.3% for red blood cell count using dataset 1 and 97.38% for white blood cell count using dataset 2.

scholarly journals White Blood Cell Image Classification Using Deep Learning

2021 ◽  
Vol 7 (09) ◽  
pp. 7-12
Author(s):  
Neerukattu Indrani and Chiraparapu Srinivasa Rao
Keyword(s):  
Deep Learning ◽  
Blood Cells ◽  
White Blood Cells ◽  
Training Phase ◽  
Counting System ◽  
Cell Image ◽  
Area Of Interest ◽  
Manual Counting ◽  
Blood Smears ◽  
Microscopic Inspection

The microscopic inspection of blood smears provides diagnostic information concerning patients’ health status. For example, the presence of infections, leukemia, and some particular kinds of cancers can be diagnosed based on the results of the classification and the count of white blood cells. The traditional method for the differential blood count is performed by experienced operators. They use a microscope and count the percentage of the occurrence of each type of cell counted within an area of interest in smears. Obviously, this manual counting process is very tedious and slow. In addition, the cell classification and counting accuracy may depend on the capabilities and experiences of the operators. Therefore, the necessity of an automated differential counting system becomes inevitable. In this paper, CNN models are used. In order to achieve good performance from deep learning methods, the network needs to be trained with large amounts of data during the training phase. We take the images of the white blood cells for the training phase and train our model on them. With this method we achieved good accuracy than traditional methods. And we can generate the results within the seconds also.

scholarly journals Hematologic Changes in Patients with Graves’ disease in Gorgan During 2014-2015

2017 ◽  
Vol 2 (3) ◽  
pp. 106
Author(s):  
Maryam Zahedi ◽  
Farzam Mirkamali ◽  
Sharabeh Hezarkhani ◽  
Armineh Motiee ◽  
Arash Rezaei Shahmirzadi ◽  
...  
Keyword(s):  
Blood Cells ◽  
Complete Blood Count ◽  
Statistical Significance ◽  
White Blood Cells ◽  
P Value ◽  
Graves Disease ◽  
Thyroid Disorder ◽  
Cross Sectional ◽  
Mild Anemia ◽  
Before And After

Background: The most common cause of hyperthyroidism in areas without iodine deficiency is Graves’ disease. There are reports of some hematological alterations in hyperthyroidism. This study was designed to measure the hematologic profile in the patients with Graves’ disease before and after the treatment.Methods: In this cross-sectional study, 100 patients were selected with convenience sampling that diagnosed as autoimmune Graves’ disease in our academic endocrinology clinic during 2014-2015. Inclusion criteria included autoimmune hyperthyroidism in patients who were referred to this center during the study period. Patients who refused to take part in the research, had recent infections disease, malignancies, surgical procedures, severe trauma, received immunosuppressive drugs or corticosteroids, high erythrocyte sedimentation rate (ESR) values during the last six months, and not responded to treatment with methimazole were excluded from the study. The simple sampling technique was used to select the patients.   A complete blood count (CBC) was taken before and after treatment. The P-value less than 0.05 was considered as the statistical significance level. All data were analyzed using the Statistical Package for the Social Sciences 16.0 (SPSS Inc., Chicago, IL, USA) software.Results: One hundred patients with a mean age of 38 ± 9.8 years were included. There were no significant changes in the white blood cells (WBC) count, red blood cells (RBC) count, and platelets. Mild anemia (Hb=12.16±1.23) present before treating the hyperthyroidism that was significantly improved after treatment (P= 0.000). Conclusions: Our results showed that the only significant hematologic change in patients with Graves’ disease was mild anemia that improves after treating the underlying thyroid disorder. 

scholarly journals Haematological Changes in Donors Post Apheresis

2021 ◽  
Author(s):  
. Nikhil ◽  
Subhashish Das ◽  
. Snigdha
Keyword(s):  
Platelet Count ◽  
Blood Cells ◽  
Complete Blood Count ◽  
White Blood Cells ◽  
Haemoglobin Concentration ◽  
Point Of View ◽  
Haematological Parameters ◽  
Cross Sectional ◽  
Wbc Count ◽  
Rbc Count

Introduction: The productivity, quality of platelet apheresis collection has improved because of the considerable advancement in the automated cell separators. Automated cell separators have lot of sizeable scientific advances, but the alertness has been centered to Platelet Concentrates (PCs) quality than on safety of donor. Aim: To find the changes in haematological parameters and the consequences of apheresis and plateletpheresis on donor’s health. Materials and Methods: It was observational cross-sectional study done in laboratory at RL Jalappa Blood Bank, Tamaka, Kolar, Karnataka, India. The study was done from March 2019 to August 2020. A total of 300 healthy donors (plateletpheresis donors) were involved in the study. The plateletpheresis (Haemonetics MCS), predonation and postdonation haematological parameters such as haemoglobin concentration, Haematocrit (Hct), platelet, white and red blood cell count were calculated in all donors. The samples for Complete Blood Count (CBC) were secured from the donors, at the beginning and end of the procedure. Postdonation haematological parameters such as platelet count, haemoglobin, haematocrit, White Blood Cells (WBC), Red Blood Cells (RBC) counts of the donor was inscribed and comparison was done with the pre donation haematological parameters. Quality control of all Single Donor Platelet (SDP) products was done. All donors were evaluated for adverse donor reactions. The mean pre and post plateletpheresis values comparison was done utilising paired t-test. Statistical analysis was accomplished utilising Statistical Package for the Social Sciences (SPSS) software version 16.0. Results: Platelet count, haemoglobin, WBC count, RBC count and haematocrit were jotted down from 262 donors and a significant decrease was noticed in these parameters postdonation. Donor parameter platelet count (lac/mL) value was decreased from 273.57-224.28 whereas WBC count (cu/mm) predonation value decreased from 9.91-8.86 Postdonation, haemoglobin (g/dL) value decreased from 14.46-12.91, haematocrit (%) decreased slightly from 45.19-44.19, RBC count (million/mm3) decreased from 5.21-5.01. This concluded that the values decreased postdonation. Conclusion: The study conducted was safe from donor’s point of view. SDP is very effective in treatment of thrombocytopenia and is safe from recipient’s point of view.

scholarly journals Blob Detection and Deep Learning for Leukemic Blood Image Analysis

2020 ◽  
Vol 10 (3) ◽  
pp. 1176
Author(s):  
Cecilia Di Ruberto ◽  
Andrea Loddo ◽  
Giovanni Puglisi
Keyword(s):  
Blood Cells ◽  
Complete Blood Count ◽  
State Of The Art ◽  
White Blood Cells ◽  
Blob Detection ◽  
Manual Inspection ◽  
Blood Smear Examination ◽  
Novel Method ◽  
Public Datasets ◽  
Processing Techniques

In microscopy, laboratory tests make use of cell counters or flow cytometers to perform tests on blood cells, like the complete blood count, rapidly. However, a manual blood smear examination is still needed to verify the counter results and to monitor patients under therapy. Moreover, the manual inspection permits the description of the cells’ appearance, as well as any abnormalities. Unfortunately, manual analysis is long and tedious, and its result can be subjective and error-prone. Nevertheless, using image processing techniques, it is possible to automate the entire workflow, both reducing the operators’ workload and improving the diagnosis results. In this paper, we propose a novel method for recognizing white blood cells from microscopic blood images and classify them as healthy or affected by leukemia. The presented system is tested on public datasets for leukemia detection, the SMC-IDB, the IUMS-IDB, and the ALL-IDB. The results are promising, achieving 100% accuracy for the first two datasets and 99.7% for the ALL-IDB in white cells detection and 94.1% in leukemia classification, outperforming the state-of-the-art.

Performance evaluation of Sysmex XN hematology analyzer in umbilical cord blood: a comparison study with Sysmex XE-2100

2014 ◽  
Vol 52 (12) ◽  
Author(s):  
Hanah Kim ◽  
Mina Hur ◽  
Sang-Gyeu Choi ◽  
Hee-Won Moon ◽  
Yeo-Min Yun ◽  
...  
Keyword(s):  
Cord Blood ◽  
Umbilical Cord ◽  
Umbilical Cord Blood ◽  
Blood Cells ◽  
Complete Blood Count ◽  
White Blood Cells ◽  
Modular System ◽  
Hematopoietic Stem ◽  
Nucleated Red Blood Cells ◽  
Hematology Analyzer

AbstractThe Sysmex XN (XN) modular system (Sysmex, Kobe, Japan) is a new automated hematology analyzer equipped with different principles from its previous version, Sysmex XE-2100. We compared the performances of Sysmex XN and XE-2100 in umbilical cord blood (CB) specimens.In 160 CB specimens, complete blood count (CBC) parameters and white blood cells (WBC) differentials were compared between the two analyzers. Their flagging performances for blasts, abnormal/atypical lymphocytes, immature granulocytes and/or left-shift (IG), and nucleated red blood cells (NRBC) counts were compared with manual counts. For the blast flagging, Q values by Sysmex XN were further compared with manual slide review.Sysmex XN and XE-2100 showed high or very high correlations for most CBC parameters but variable correlations for WBC differentials. Compared with XE-2100, XN showed significantly different flagging performances for blasts, abnormal/atypical lymphocytes, and IG. The flagging efficiency for blasts was significantly better on Sysmex XN than on XE-2100 (85.0% vs. 38.8%): Sysmex XN showed a remarkably increased specificity of blast flag, compromising its sensitivity of blast flag. Among the 24 specimens with blasts (range, 0.5%–1.5%), only one (4.2%) showed a positive Q value.This study highlighted the remarkable differences of flagging performances between Sysmex XN and XE-2100 in CB specimens. The Sysmex XN modular system seems to be a suitable and practical option for the CB specimens used for hematopoietic stem cell transplantation as well as for the specimens from neonates.

scholarly journals Case Report: Angiostrongylus cantonesis Myelitis in Thailand

2021 ◽  
Author(s):  
Tharatorn Nuntawit ◽  
Wantin Sribenjalux ◽  
Atibordee Meesing
Keyword(s):  
Blood Cells ◽  
Complete Blood Count ◽  
White Blood Cells ◽  
Complete Recovery ◽  
Conus Medullaris ◽  
Resonance Imaging ◽  
Test Kit ◽  
Hemorrhagic Lesion ◽  
The Right ◽  
Raw Shrimp

A 67-year-old man presented with headache, middle back pain that radiated to both legs, and paresthesia in the right leg for 1 day. He had eaten raw shrimp 1 week previously. Over the next week after admission, he developed urinary retention and weakness in both legs. The numbness in his right leg expanded to below the umbilicus. Magnetic resonance imaging of the spinal cord showed myelopathy with minimal cord swelling at T9 to the conus medullaris and a hemorrhagic lesion from T10 to T11. A complete blood count on day 28 after the onset of symptoms revealed leukocytosis without eosinophilia and no white blood cells in his cerebrospinal fluid. Results of an immunochromatographic test kit were positive for Angiostrongylus cantonesis but negative for Gnathostoma spinigerum. After a 4-week course of albendazole combined with a tapering dose of dexamethasone, he achieved nearly complete recovery.

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