scholarly journals Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning

BME Frontiers ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
DongHun Ryu ◽  
Jinho Kim ◽  
Daejin Lim ◽  
Hyun-Seok Min ◽  
In Young Yoo ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Refractive Index ◽  
Deep Learning ◽  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
Hematologic Malignancy ◽  
Label Free ◽  
Cell Classification

Objective and Impact Statement. We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index (RI) tomography. Our computational approach that fully utilizes tomographic information of bone marrow (BM) white blood cell (WBC) enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research. Introduction. Conventional methods for examining blood cells, such as blood smear analysis by medical professionals and fluorescence-activated cell sorting, require significant time, costs, and domain knowledge that could affect test results. While label-free imaging techniques that use a specimen’s intrinsic contrast (e.g., multiphoton and Raman microscopy) have been used to characterize blood cells, their imaging procedures and instrumentations are relatively time-consuming and complex. Methods. The RI tomograms of the BM WBCs are acquired via Mach-Zehnder interferometer-based tomographic microscope and classified by a 3D convolutional neural network. We test our deep learning classifier for the four types of bone marrow WBC collected from healthy donors (n=10): monocyte, myelocyte, B lymphocyte, and T lymphocyte. The quantitative parameters of WBC are directly obtained from the tomograms. Results. Our results show >99% accuracy for the binary classification of myeloids and lymphoids and >96% accuracy for the four-type classification of B and T lymphocytes, monocyte, and myelocytes. The feature learning capability of our approach is visualized via an unsupervised dimension reduction technique. Conclusion. We envision that the proposed cell classification framework can be easily integrated into existing blood cell investigation workflows, providing cost-effective and rapid diagnosis for hematologic malignancy.

scholarly journals Label-free bone marrow white blood cell classification using refractive index tomograms and deep learning

2020 ◽  
Author(s):  
DongHun Ryu ◽  
Jinho Kim ◽  
Daejin Lim ◽  
Hyun-Seok Min ◽  
Inyoung You ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Refractive Index ◽  
Deep Learning ◽  
Blood Cell ◽  
White Blood Cell ◽  
Hematologic Malignancy ◽  
White Blood Cells ◽  
Label Free ◽  
Cell Classification

AbstractIn this study, we report a label-free bone marrow white blood cell classification framework that captures the three-dimensional (3D) refractive index (RI) distributions of individual cells and analyzes with deep learning. Without using labeling or staining processes, 3D RI distributions of individual white blood cells were exploited for accurate profiling of their subtypes. Powered by deep learning, our method used the high-dimensional information of the WBC RI tomogram voxels and achieved high accuracy. The results show >99 % accuracy for the binary classification of myeloids and lymphoids and >96 % accuracy for the four-type classification of B, T lymphocytes, monocytes, and myelocytes. Furthermore, the feature learning of our approach was visualized via an unsupervised dimension reduction technique. We envision that this framework can be integrated into existing workflows for blood cell investigation, thereby providing cost-effective and rapid diagnosis of hematologic malignancy.

scholarly journals Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

2019 ◽  
Vol 9 (2) ◽  
pp. 173
Author(s):  
Apri Nur Liyantoko ◽  
Ika Candradewi ◽  
Agus Harjoko
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Back Propagation ◽  
Cell Types ◽  
Diagnostic Process ◽  
The Subject ◽  
Processing Techniques

 Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and  multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

scholarly journals A Neural-Network-Based Approach to White Blood Cell Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mu-Chun Su ◽  
Chun-Yen Cheng ◽  
Pa-Chun Wang
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Classification System ◽  
Blood Cells ◽  
White Blood Cells ◽  
Recognition Rate ◽  
Color Space ◽  
Segmentation Algorithm ◽  
Cell Classification ◽  
Morphological Process

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

Reversible Neutropenia Associated with Ampicillin Therapy in Pediatric Patients

1981 ◽  
Vol 15 (10) ◽  
pp. 802-806 ◽  
Author(s):  
Kusum Kumar ◽  
Ashir Kumar
Keyword(s):  
Bone Marrow ◽  
Blood Cell ◽  
Red Blood Cells ◽  
White Blood Cell ◽  
Blood Cells ◽  
Pediatric Patients ◽  
Bone Marrow Examination ◽  
High Dose ◽  
Maturation Arrest ◽  
High Doses

Hematologic abnormalities associated with penicillin compounds are uncommon, and neutropenia associated with ampicillin is reported even less frequently. Neutropenia developed in three pediatric patients after high-dose (150–400 mg/kg) ampicillin therapy over a period of 3 to 12 days. In all cases, the white blood cell and neutrophil counts returned towards normal within 4 to 11 days after discontinuation of the antibiotic. Bone marrow examination revealed a maturation arrest in one and slight shift to the left in the maturation of granulocytic cells in another. Other marrow components were normal. Red blood cells, reticulocytes, platelets, and hemoglobin did not show any abnormal alteration in any of the patients. Physicians administering ampicillin, particularly in high doses, should be alert to the possible development of neutropenia; however, all reported neutropenias have been reversible.

scholarly journals Possible automatic cell classification of bone marrow aspirate using the CELL-DYN 4000®automatic blood cell analyzer

2002 ◽  
Vol 16 (2) ◽  
pp. 86-90 ◽  
Author(s):  
Ryousuke Yamamura ◽  
Takahisa Yamane ◽  
Masayuki Hino ◽  
Kensuke Ohta ◽  
Hisako Shibata ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Blood Cell ◽  
Bone Marrow Aspirate ◽  
Cell Classification ◽  
Cell Analyzer

scholarly journals Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

2019 ◽  
Vol 25 (5) ◽  
pp. 63-68 ◽  
Author(s):  
Mesut Togacar ◽  
Burhan Ergen ◽  
Mehmet Emre Sertkaya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Immune System ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Network Models ◽  
Neural Network Models

The white blood cells produced in the bone marrow and lymphoid tissue known as leucocytes are an important part of the immune system to protect the body against foreign invaders and infectious disease. These cells, which do not have color, have a few days or several weeks of life. A lot of clinic experience is required for a doctor to detect the amount of white blood cells in human blood and classify it. Thus, early and accurate diagnosis can be made in the formation of various disease types, including infection on the immune system, such as anemia and leukemia, while evaluating and determining the disease of a patient. The white blood cells can be separated into four subclasses, such as Eosinophil, Lymphocyte, Monocyte, and Neutrophil. This study focuses on the separation of the white blood cell images by the classification process using convolutional neural network models, which is a deep learning model. A deep learning network, which is slow in the training step due to the complex architecture, but fast in the test step, is used for the feature extraction instead of intricate methods. For the subclass separation of white blood cells, the experimental results show that the AlexNet architecture gives the correct recognition rate among the convolutional neural network architectures tested in the study. Various classifiers are performed on the features derived from the AlexNet architecture to evaluate the classification performance. The best performance in the classification of white blood cells is given by the quadratic discriminant analysis classifier with the accuracy of 97.78 %.

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