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 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.

The Immature Reticulocyte Fraction Predicts Marrow Recovery and Can Obviate the Need for Bone Marrow Examination in Persistently Neutropenic Cancer Patients

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 4526-4526
Author(s):  
Sajjad Haider ◽  
Lakshmikanth Katragadda ◽  
Jameel Muzaffar ◽  
Monica Grazziutti ◽  
Muhammad R Akbar ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Blood Cell ◽  
Cancer Patients ◽  
White Blood Cell ◽  
Bone Marrow Aspirate ◽  
Conflicts Of Interest ◽  
Bone Marrow Examination ◽  
Clinical Value ◽  
Myeloablative Chemotherapy ◽  
Increased Risk

Abstract Abstract 4526 Introduction: Prolonged and persistent neutropenia (white blood cell (WBC) ≤1000/μL) after chemotherapy is associated with increased risk for infection in cancer patients and is often an indication for a diagnostic bone marrow aspirate and biopsy. A test that predicts WBC recovery ≥ 1000/μL and that obviates the need for marrow sampling would be of clinical value. The immature reticulocyte fraction (IRF) reflects erythroid production and hence a recovering marrow. Materials and Methods: We identified 17 myeloma patients with prolonged pancytopenia after either myeloablative or non-myeloablative chemotherapy between March 2010 and February 2011, and compared the time of occurrence of IRF doubling (IRF-D) to the findings on bone marrow examination. Results: The time to IRF doubling preceded increase of WBC ≥ 1000/μL in 15 of 17 patients by a mean of 4.5 days (range 0–18 days). In two patients, the IRF-D coincided with WBC ≥ 1000/μL. The IRF doubled 2–4 days before the bone marrow examination in four patients and at a mean of 3.7 days (range 1–13 days) after the marrow examination in the remaining 13. Conclusion: We conclude that the IRF-D is a simple, inexpensive and widely available test that can precede marrow recovery by several days and may therefore obviate the need for a diagnostic bone marrow aspirate and biopsy in patients with prolonged and persistent neutropenia. Disclosures: No relevant conflicts of interest to declare.

scholarly journals A Completely Digital Workflow for Differentials in Bone Marrow Cytomorphology Supported By Machine Learning Provides Promising Results in Object Detection

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4922-4922
Author(s):  
Christian Pohlkamp ◽  
Niroshan Nadarajah ◽  
Inseok Heo ◽  
Dimitros Tziotis ◽  
Sven Maschek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bone Marrow ◽  
High Resolution ◽  
Blood Cell ◽  
Object Detection ◽  
Single Cell ◽  
Bone Marrow Cell ◽  
Marrow Cell ◽  
Training Data

Abstract Background: Cytomorphology is an essential method to assess disease phenotypes. Recently, promising results of automation, digitalization and machine learning (ML) for this gold standard have been demonstrated. We reported on successful integration of such workflows into our lab routine, including automated scanning of peripheral blood smears and ML-based classification of blood cell images (ASH 2020). Following this pilot project, we are focusing on an equivalent approach for bone marrow. Aim: To establish a multistep-approach including scan of bone marrow smears and detection/classification of all kinds of bone marrow cell types in healthy individuals and leukemia patients. Methods: The method includes a pre-scan at 10x magnification for detecting suitable "areas of interest" (AOI) for cytomorphological analysis, a high resolution capture of a predefinable number of AOI at 40x magnification (always using oil) and an automated object detection and classification. For all scanning tasks, a Metafer Scanning System (Zeiss Axio Imager.Z2 microscope, automatic slide feeder SFx80 and automated oil disperser) from MetaSystems (Altlussheim, GER) was used. To generate training data for AOI detection, 37 bone marrow smears were scanned at 10x magnification. 6 different quality classes of regions (based on number and distribution of cells) were annotated by hem experts using polygons. In total, 185,000 grid images were extracted from the annotated regions and used for training a deep neural network (DNN) to distinguish the 6 quality classes and to generate a position list for a high resolution scan (40x magnification). In addition, we scanned the labeled AOI of 68 smears at 40x magnification, acquiring colour images (2048x1496 pixels) of bone marrow cell layers. Each single cell was labeled by human investigators using rectangular bounding boxes (in total: 47,118 cells in 511 images). We set up a supervised ML model, using the labeled 40x images as an input. We fine-tuned the COCO dataset pre-trained YOLOv5 model with our dataset and evaluated using 5-fold cross valuation. To reduce overfitting, image augmentation algorithms were applied. Results: Our first DNN was able to detect (10x magnification) and capture (40x magnification) AOI in bone marrow smears, sorted by quality and in acceptable time spans. Average time for the 10x pre-scan was 6 min. From the resulting position list, the 50 positions with highest quality values were acquired at an average of 1:30 min. Our second, independent DNN was able to detect nucleated cells at 94% sensitivity and 75% precision in unlabeled bone marrow images (40x magnification). In this model, we overweighted recall over precision (5:1) to avoid missing any objects of interest, assuming that false positive labels could be corrected by human investigators when reviewing digital images. For the classification of single cells, a third independent DNN will be necessary. Actually, different approaches are being tested, including our existing blood cell classifier and a former collaborative bone marrow classification model based on a training set of 100,000 annotated bone marrow cells. Depending on these results, new training data for generation of a completely new model could be assessed. The two existing models enable a fully automated digital workflow including scan of bone marrow smears and delivery of single cell image galleries for human classification already now. Conclusion: We here present solutions for multiple-DNN-based tools for bone marrow cytomorphology. They allow working digitally and remotely in routine diagnostics. Final solutions will offer single cell classifications and galleries for human review and include real time training of respective classifier models with dynamic datasets. Figure 1 Figure 1. Disclosures Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence

2020 ◽  
Vol 64 (6) ◽  
pp. 588-596
Author(s):  
Xinyan Fu ◽  
May Fu ◽  
Qiang Li ◽  
Xiangui Peng ◽  
Ju Lu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bone Marrow ◽  
Sensitivity And Specificity ◽  
Bone Marrow Aspirate ◽  
Marrow Cell ◽  
Clinical Applications ◽  
Differential Count ◽  
Cell Classification ◽  
Cell Percentage ◽  
Differential Cell

<b><i>Introduction:</i></b> The nucleated-cell differential count on the bone marrow aspirate smears is required for the clinical diagnosis of hematological malignancy. Manual bone marrow differential count is time consuming and lacks consistency. In this study, a novel artificial intelligence (AI)-based system was developed to perform cell automatic classification of bone marrow cells and determine its potential clinical applications. <b><i>Materials and Methods:</i></b> Bone marrow aspirate smears were collected from the Xinqiao Hospital of Army Medical University. First, an automated analysis system (<i>Morphogo</i>) scanned and generated whole digital images of bone marrow smears. Then, the nucleated marrow cells in the selected areas of the smears at a magnification of ×1,000 were analyzed by the software utilizing an AI-based platform. The cell classification results were further reviewed and confirmed independently by 2 experienced pathologists. The automatic cell classification performance of the system was evaluated using 3 categories: accuracy, sensitivity, and specificity. Correlation coefficients and linear regression equations between automatic cell classification by the AI-based system and concurrent manual differential count were calculated. <b><i>Results:</i></b> In 230 cases, the classification accuracy was above 85.7% for hematopoietic lineage cells. Averages of sensitivity and specificity of the system were found to be 69.4 and 97.2%, respectively. The differential cell percentage of the automated count based on 200–500 cell counts was correlated with differential cell percentage provided by the pathologists for granulocytes, erythrocytes, and lymphocytes (<i>r</i> ≥ 0.762, <i>p</i> &#x3c; 0.001). <b><i>Discussion/Conclusion:</i></b> This pilot study confirmed that the <i>Morphogo</i> system is a reliable tool for automatic bone marrow cell differential count analysis and has potential for clinical applications. Current ongoing large-scale multicenter validation studies will provide more information to further confirm the clinical utility of the system.

scholarly journals An Exploration of the Image Processing Techniques for the Detection of Leukemia

2018 ◽  
Vol 7 (2) ◽  
pp. 96-99
Author(s):  
A. Premnath ◽  
V. S. Meenakshi
Keyword(s):  
Image Processing ◽  
Bone Marrow ◽  
Blood Cell ◽  
Diagnostic Method ◽  
Cell Classification ◽  
Image Processing Techniques ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Processing Techniques

In the pathological diagnostic method, categorization of blood cell has more essential to detect and analyze the disease. The complications that are connected with blood can be distributed only after the blood cell classification. The illness that begins with the bone marrow is the Leukemia. Therefore, it must be handled at the beginning step and proceeds to death if continuing untreated. This present research elucidates an investigation of diagnosing leukemia from microscopic blood image exhausting various image processing algorithms.

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