Performance of the new MC-80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600.

INTRODUCTION: Mindray MC-80 is an automated system for digital imaging of white blood cells (WBCs) and their pre-classification. The objective of this work is to analyse its performance comparing it with the CellaVision® DM9600. METHODS: A total of 445 samples were used, 194 normal and 251 abnormal: acute leukaemia (100), myelodysplastic syndromes/myeloproliferative neoplasms (33), lymphoid neoplasms (50), plasma cell neoplasms (14), infections (49) and thrombocytopenia (5). WBC pre-classification values with the MC-80 and DM9600 were compared with (1) the microscope, (2) Mindray BC-6800Plus differentials in only normal samples, and (3) confirmed or reclassified images (post-classification). Pearson's correlation, Lin's concordance, Passing-Bablok regression, and Bland-Altman plots were used. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for abnormal cells using the MC-80 were calculated. RESULTS: The PPV and NPV were above 98% and 99%, for normal samples. For immature granulocytes (IG), NPV and PPV were 100% and 74.2%. When comparing the WBC differentials using the MC-80, the microscope and the BC-6800Plus, no differences were found except for basophils and IG. Our results showed good agreement between the pre- and post-classification of normal WBC, including IG, quantified by high correlation and concordance values (0.91-1). Sensitivity and specificity for blasts were 0.984 and 0.640. The MC-80 detected abnormal lymphocytes in 30% of the smears from patients with lymphoid neoplasm. Plasma cell identification was better using the DM9600. The sensitivity and specificity for erythroblast detection were 1 and 0.890. CONCLUSION: We found that the MC-80 shows high performance for WBC differentials for both normal samples and patients with haematological diseases.

Kimura disease as an uncommon cause of persistent hypereosinophilia: a diagnostic challenge.

Kimura disease (KD) is an unusual inflammatory disease of unknown etiology. Despite being described many years ago, KD might cause diagnostic difficulty or be confused with other conditions. Here, we present a 33-year-old Filipino woman who was referred to our hospital for evaluation of persistent eosinophilia and intense pruritus. Blood analysis and peripheral blood smear review showed high eosinophil counts (3.8 x109/L, 40%) that did not show morphological abnormalities. Besides, high serum IgE concentration was detected (33,528 kU/L). Serological tests were positive for Toxocara canis and treatment with albendazol was initiated. Nevertheless, increased eosinophil counts were still present after several months, alongside with high serum IgE concentrations and intense pruritus. During her follow-up, an inguinal adenopathy was detected. The biopsy revealed lymphoid hyperplasia with reactive germinal centers and massive eosinophil infiltration. Proteinaceous deposits of eosinophilic material were also observed. All these findings, together with peripheral blood eosinophilia and high IgE concentrations, confirmed the diagnosis of KD. The diagnosis of KD should be considered in the differential diagnosis of long-standing unexplained eosinophilia in association with high IgE concentrations, pruritus and lymphadenopathies.

Automatic generation of artificial images of leukocytes and leukemic cells using generative adversarial networks (syntheticcellgan).

BACKGROUND AND OBJECTIVES: Visual analysis of cell morphology has an important role in the diagnosis of hematological diseases. Morphological cell recognition is a challenge that requires experience and in-depth review by clinical pathologists. Within the new trend of introducing computer-aided diagnostic tools in laboratory medicine, models based on deep learning are being developed for the automatic identification of different types of cells in peripheral blood. In general, well-annotated large image sets are needed to train the models to reach a desired classification performance. This is especially relevant when it comes to discerning between cell images in which morphological differences are subtle and when it comes to low prevalent diseases with the consequent difficulty in collecting cell images. The objective of this work is to develop, train and validate SyntheticCellGAN (SCG), a new system for the automatic generation of artificial images of white blood cells, maintaining morphological characteristics very close to real cells found in practice in clinical laboratories. METHODS: SCG is designed with two sequential generative adversarial networks. First, a Wasserstein structure is used to transform random noise vectors into low resolution images of basic mononuclear cells. Second, the concept of image-to-image translation is used to build specific models that transform the basic images into high-resolution final images with the realistic morphology of each cell type target: 1) the five groups of normal leukocytes (lymphocytes, monocytes, eosinophils, neutrophils and basophils); 2) atypical promyelocytes and hairy cells, which are two relevant cell types of complex morphology with low abundance in blood smears. RESULTS: The images of the SCG system are evaluated with four experimental tests. In the first test we evaluated the generated images with quantitative metrics for GANs. In the second test, morphological verification of the artificial images is performed by expert clinical pathologists with 100% accuracy. In the third test, two classifiers based on convolutional neural networks (CNN) previously trained with images of real cells are used. Two sets of artificial images of the SCG system are classified with an accuracy of 95.36% and 94%, respectively. In the fourth test, three CNN classifiers are trained with artificial images of the SCG system. Real cells are identified with an accuracy ranging from 87.7% to 100%. CONCLUSIONS: The SCG system has proven effective in creating images of all normal leukocytes and two low-prevalence cell classes associated with diseases such as acute promyelocyte leukemia and hairy cell leukemia. Once trained, the system requires low computational cost and can help augment high-quality image datasets to improve automatic recognition model training for clinical laboratory practice.

Serous body fluid evaluation using the new automated haematology analyser Mindray BC-6800Plus.

OBJECTIVES: Cellular analysis of body fluids (BF) has clinical relevance in several medical conditions. The objective of this study is twofold: (1) evaluate the analytical performance of the BF mode of Mindray BC-6800 Plus compared to manual counts under microscopy and (2) analyse if the high-fluorescent cell counts provided by the analyser (HF-BF) are useful to detect malignancy. METHODS: A total of 285 BF was analysed: 250 corresponding to patients without neoplasia and 35 to patients with malignant diseases. Manual differential counts were performed in BF with ≥250 cells/µL. Percentages and absolute counts were obtained on the BC-6800Plus for total nucleated cells (TC-BF), mononuclear, polymorphonuclear and HF-BF. Statistical analysis was performed using Mann-Whitney U-test, Spearman's correlation, Passing-Bablok regression, Bland-Altman graph and ROC curve. RESULTS: To compare manual and automatic total cell counts, samples were divided in three groups: <250, 250-1,000 and >1,000 cells/µL. Correlation was good in all cases (r=0.72, 0.73 and 0.92, respectively) without significant differences between both methods (p=0.65, 0.39 and 0.30, respectively). The concordance between methods showed values of 90%. Considering malignant samples, median HF-BF values showed significant higher values (102 cells/µL) with respect to non-malignant (4 cells/µL) (p<0.001). The cut-off value of 8.5 HF-BF/µL was able to discriminate samples containing malignant cells showing sensitivity and specificity values of 89 and 71%, respectively. Considering both, HF-BF and TC-BF values, sensitivity and specificity values were 100 and 53%, respectively. CONCLUSIONS: This study reveals that the Mindray BC-6800Plus offers an accurate and acceptable performance, showing results consistent with the manual method. It is recommended to consider both HF-BF and TC-BF values for the screening of the microscopic evaluation to ensure the detection of all malignant samples.

Quantitative features to assist in the diagnostic assessment of chronic lymphocytic leukemia progression†.

The use of artificial intelligence methods in the image-based diagnostic assessment of hematological diseases is a growing trend in recent years. In these methods, the selection of quantitative features that describe cytological characteristics plays a key role. They are expected to add objectivity and consistency among observers to the geometric, color, or texture variables that pathologists usually interpret from visual inspection. In a recent paper in The Journal of Pathology, El Hussein, Chen et al proposed an algorithmic procedure to assist pathologists in the diagnostic evaluation of chronic lymphocytic leukemia (CLL) progression using whole-slide image analysis of tissue samples. The core of the procedure was a set of quantitative descriptors (biomarkers) calculated from the segmentation of cell nuclei, which was performed using a convolutional neural network. These biomarkers were based on clinical practice and easily calculated with reproducible tools. They were used as input to a machine learning algorithm that provided classification in one of the stages of CLL progression. Works like this can contribute to the integration into the workflow of clinical laboratories of automated diagnostic systems based on the morphological analysis of histological slides and blood smears. © 2021 The Pathological Society of Great Britain and Ireland.

A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes.

BACKGROUND: Dysplastic neutrophils commonly show at least 2/3 reduction of the content of cytoplasmic granules by morphologic examination. Recognition of less granulated dysplastic neutrophils by human eyes is difficult and prone to inter-observer variability. To tackle this problem, we proposed a new deep learning model (DysplasiaNet) able to automatically recognize the presence of hypogranulated dysplastic neutrophils in peripheral blood. METHODS: Eight models were generated by varying convolutional blocks, number of layer nodes and fully connected layers. Each model was trained for 20 epochs. The five most accurate models were selected for a second stage, being trained again from scratch for 100 epochs. After training, cut-off values were calculated for a granularity score that discerns between normal and dysplastic neutrophils. Furthermore, a threshold value was obtained to quantify the minimum proportion of dysplastic neutrophils in the smear to consider that the patient might have a myelodysplastic syndrome (MDS). The final selected model was the one with the highest accuracy (95.5%). RESULTS: We performed a final proof of concept with new patients not involved in previous steps. We reported 95.5% sensitivity, 94.3% specificity, 94% precision, and a global accuracy of 94.85%. CONCLUSIONS: The primary contribution of this work is a predictive model for the automatic recognition in an objective way of hypogranulated neutrophils in peripheral blood smears. We envision the utility of the model implemented as an evaluation tool for MDS diagnosis integrated in the clinical laboratory workflow.

A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images.

BACKGROUND AND OBJECTIVES: Morphological differentiation among blasts circulating in blood in acute leukaemia is challenging. Artificial intelligence decision support systems hold substantial promise as part of clinical practise in detecting haematological malignancy. This study aims to develop a deep learning-based system to predict the diagnosis of acute leukaemia using blood cell images. METHODS: A set of 731 blood smears containing 16,450 single-cell images was analysed from 100 healthy controls, 191 patients with viral infections and 148 with acute leukaemia. Training and testing sets were arranged with 85% and 15% of these smears, respectively. To find the best architecture for acute leukaemia classification VGG16, ResNet101, DenseNet121 and SENet154 were evaluated. Fine-tuning was implemented to these pre-trained CNNs to adapt their layers to our data. Once the best architecture was chosen, a system with two modules working sequentially was configured (ALNet). The first module recognised abnormal promyelocytes among other mononuclear blood cell images, such as lymphocytes, monocytes, reactive lymphocytes and blasts. The second distinguished if blasts were myeloid or lymphoid lineage. The final strategy was to predict patients' initial diagnosis of acute leukaemia lineage using the blood smear review. ALNet was assessed with smears of the testing set. RESULTS: ALNet provided the correct diagnostic prediction of all patients with promyelocytic and myeloid leukaemia. Sensitivity, specificity and precision values of 100%, 92.3% and 93.7%, respectively, were obtained for myeloid leukaemia. Regarding lymphoid leukaemia, a sensitivity of 89% and specificity and precision values of 100% were obtained. CONCLUSIONS: ALNet is a predictive model designed with two serially connected convolutional networks. It is proposed to assist clinical pathologists in the diagnosis of acute leukaemia during the blood smear review. It has been proved to distinguish neoplastic (leukaemia) and non-neoplastic (infections) diseases, as well as recognise the leukaemia lineage.

Peripheral blood morphology review and diagnostic proficiency evaluation by a new Spanish EQAS during the period 2011-2019.

INTRODUCTION: The Spanish Haematology and Haemotherapy Society organizes peripheral blood smear review scheme, focused on the evaluation of diagnostic proficiency of participants by blood cell morphology analysis. The objective was to evaluate the efficacy of this scheme as an educational tool to improve the diagnostic proficiency of the participants. METHODS: During 2011-2019, 54 peripheral blood smears, alongside with patient details such as age, sex, blood cell counts and relevant clinical information, were sent to an average of 125 ± 13 laboratories per year. A number of 44 shipments were selected to analyse whether successive surveys of the same disease may lead to an improvement in the diagnostic success rate proposed by the laboratories. Participants were asked to select the most relevant morphological abnormalities, alongside the diagnostic orientation. Agreement of participant responses with RR was evaluated. RESULTS: Spanish laboratories showed a diagnostic proficiency greater than 80% in acute myeloid leukaemia, including acute promyelocytic leukaemia, mature B-cell neoplasms (hairy cell leukaemia and splenic marginal zone lymphoma), chronic myeloid leukaemia, sickle cell disease, Bernard-Soulier syndrome and infectious mononucleosis. It was important to note the significant improvement over the time in the successive shipments of the same disease, with a 31% and 13% increase in their diagnostic orientation success rate for acute myeloid leukaemia and acute promyelocytic leukaemia cases, respectively, 15% for mantle cell lymphoma and 6% for sickle cell disease. CONCLUSIONS: The present study provides evidence that peripheral blood smear review scheme can be a valid educational tool to improve the clinical pathologist skills in blood morphology and haematological diagnosis.

A dataset of microscopic peripheral blood cell images for development of automatic recognition systems.

This article makes available a dataset that was used for the development of an automatic recognition system of peripheral blood cell images using convolutional neural networks [1]. The dataset contains a total of 17,092 images of individual normal cells, which were acquired using the analyzer CellaVision DM96 in the Core Laboratory at the Hospital Clinic of Barcelona. The dataset is organized in the following eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts and platelets or thrombocytes. The size of the images is 360 × 363 pixels, in format jpg, and they were annotated by expert clinical pathologists. The images were captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection. This high-quality labelled dataset may be used to train and test machine learning and deep learning models to recognize different types of normal peripheral blood cells. To our knowledge, this is the first publicly available set with large numbers of normal peripheral blood cells, so that it is expected to be a canonical dataset for model benchmarking.

Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.

AIMS: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagnosis of acute leukaemia using peripheral blood (PB) images. METHODS: A set of 442 smears was analysed from 206 patients. It was split into a training set with 75% of these smears and a testing set with the remaining 25%. Colour clustering and mathematical morphology were used to segment cell images, which allowed the extraction of 2,867 geometric, colour and texture features. Several classification techniques were studied to obtain the most accurate classification method. Afterwards, the classifier was assessed with the images of the testing set. The final strategy was to predict the patient's diagnosis using the PB smear, and the final assessment was done with the cell images of the smears of the testing set. RESULTS: The highest classification accuracy was achieved with the selection of 700 features with linear discriminant analysis. The overall classification accuracy for the six groups of cell types was 85.8%, while the overall classification accuracy for individual smears was 94% as compared with the true confirmed diagnosis. CONCLUSIONS: The proposed method achieves a high diagnostic precision in the recognition of different types of blast cells among other mononuclear cells circulating in blood. It is the first encouraging step towards the idea of being a diagnostic support tool in the future.

Quantitative Cytologic Descriptors to Differentiate CLL, Sézary, Granular, and Villous Lymphocytes Through Image Analysis.

OBJECTIVES: We aimed to find descriptors to identify chronic lymphocytic leukemia (CLL), Sézary, granular, and villous lymphocytes among normal and abnormal lymphocytes in peripheral blood. METHODS: Image analysis was applied to 768 images from 15 different types of lymphoid cells and monocytes to determine four discriminant descriptors. For each descriptor, numerical scales were obtained using 627 images from 79 patients. An assessment of the four descriptors was performed using smears from 209 new patients. RESULTS: Cyan correlation of the nucleus identified clumped chromatin, and standard deviation of the granulometric curve of the cyan of the nucleus was specific for cerebriform chromatin. Skewness of the histogram of the u component of the cytoplasm identified cytoplasmic granulation. Hairiness showed specificity for cytoplasmic villi. In the assessment, 96% of the smears were correctly classified. CONCLUSIONS: The quantitative descriptors obtained through image analysis may contribute to the morphologic identification of the abnormal lymphoid cells considered in this article.

Color clustering segmentation framework for image analysis of malignant lymphoid cells in peripheral blood.

Current computerized image systems are able to recognize normal blood cells in peripheral blood, but fail with abnormal cells like the classes of lymphocytes associated to lymphomas. The main challenge lies in the subtle differences in morphologic characteristics among these classes, which requires a refined segmentation. A new efficient segmentation framework has been developed, which uses the image color information through fuzzy clustering of different color components and the application of the watershed transformation with markers. The final result is the separation of three regions of interest: nucleus, entire cell, and peripheral zone around the cell. Segmentation of this zone is crucial to extract a new feature to identify cells with hair-like projections. The segmentation is validated, using a database of 4758 cell images with normal, reactive lymphocytes and five types of malignant lymphoid cells from blood smears of 105 patients, in two ways: (1) the efficiency in the accurate separation of the regions of interest, which is 92.24%, and (2) the accuracy of a classification system implemented over the segmented cells, which is 91.54%. In conclusion, the proposed segmentation framework is suitable to distinguish among abnormal blood cells with subtile color and spatial similarities. Graphical Abstract The segmentation framework uses the image color information through fuzzy clustering of different color components and the application of the watershed transformation with markers (Top). The final result is the separation of three regions of interest: nucleus, entire cell, and peripheral zone around the cell. The procedure is also validated by the implementation of a system to automatically classify different types of abnormal blood cells (Bottom).

New quantitative features for the morphological differentiation of abnormal lymphoid cell images from peripheral blood.

AIMS: This work aims to propose a set of quantitative features through digital image analysis for significant morphological qualitative features of different cells for an objective discrimination among reactive, abnormal and blast lymphoid cells. METHODS: Abnormal lymphoid cells circulating in peripheral blood in chronic lymphocytic leukaemia, B-prolymphocytic leukaemia, hairy cell leukaemia, splenic marginal zone lymphoma, mantle cell lymphoma, follicular lymphoma, T-prolymphocytic leukaemia, T large granular lymphocytic leukaemia and Sézary syndrome, normal, reactive and blast lymphoid cells were included. From 325 patients, 12 574 cell images were obtained and 2676 features (27 geometric and 2649 related to colour and texture) were extracted and analysed. RESULTS: We analysed the 20 most relevant features for the morphological differentiation of the 12 lymphoid cell groups under study. Most of them showed significant differences: 19 comparing follicular and mantle cells, 18 for blast and reactive cells, 17 for Sézary cells and T prolymphocytes and 16 for B and T prolymphocytes and 16 for villous lymphocytes. Moreover, a total of five quantitative features were significant for the discrimination among reactive and the set of abnormal lymphoid cells included. CONCLUSIONS: Image analysis may assist in quantifying cell morphology turning qualitative data into quantitative values. New cytological variables were established based on geometric and colour/texture features to contribute to a more accurate and objective morphological assessment of lymphoid cells and their association with flow cytometry methods may be interesting to explore in the next future.

Feature Analysis and Automatic Identification of Leukemic Lineage Blast Cells and Reactive Lymphoid Cells from Peripheral Blood Cell Images.

BACKGROUND: Automated peripheral blood (PB) image analyzers usually underestimate the total number of blast cells, mixing them up with reactive or normal lymphocytes. Therefore, they are not able to discriminate between myeloid or lymphoid blast cell lineages. The objective of the proposed work is to achieve automatic discrimination of reactive lymphoid cells (RLC), lymphoid and myeloid blast cells and to obtain their morphologic patterns through feature analysis. METHODS: In the training stage, a set of 696 blood cell images was selected in 32 patients (myeloid acute leukemia, lymphoid precursor neoplasms and viral or other infections). For classification, we used support vector machines, testing different combinations of feature categories and feature selection techniques. Further, a validation was implemented using the selected features over 220 images from 15 new patients (five corresponding to each category). RESULTS: Best discrimination accuracy in the training was obtained with feature selection from the whole feature set (90.1%). We selected 60 features, showing significant differences (P < 0.001) in the mean values of the different cell groups. Nucleus-cytoplasm ratio was the most important feature for the cell classification, and color-texture features from the cytoplasm were also important. In the validation stage, the overall classification accuracy and the true-positive rates for RLC, myeloid and lymphoid blast cells were 80%, 85%, 82% and 74%, respectively. CONCLUSION: The methodology appears to be able to recognize reactive lymphocytes well, especially between reactive lymphocytes and lymphoblasts.

Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis.

OBJECTIVES: The objective was the development of a method for the automatic recognition of different types of atypical lymphoid cells. METHODS: In the method development, a training set (TS) of 1,500 lymphoid cell images from peripheral blood was used. To segment the images, we used clustering of color components and watershed transformation. In total, 113 features were extracted for lymphocyte recognition by linear discriminant analysis (LDA) with a 10-fold cross-validation over the TS. Then, a new validation set (VS) of 150 images was used, performing two steps: (1) tuning the LDA classifier using the TS and (2) classifying the VS in the different lymphoid cell types. RESULTS: The segmentation algorithm was very effective in separating the cytoplasm, nucleus, and peripheral zone around the cell. From them, descriptive features were extracted and used to recognize the different lymphoid cells. The accuracy for the classification in the TS was 98.07%. The precision, sensitivity, and specificity values were above 99.7%, 97.5%, and 98.6%, respectively. The accuracy of the classification in the VS was 85.33%. CONCLUSIONS: The method reaches a high precision in the recognition of five different types of lymphoid cells and could allow for the design of a diagnosis support tool in the future.

Effects of adipose tissue-derived stem cell therapy after myocardial infarction: impact of the route of administration.

BACKGROUND: Cell-based therapies offer a promising approach to reducing the short-term mortality rate associated with heart failure after a myocardial infarction. The aim of the study was to analyze histological and functional effects of adipose tissue-derived stem cells (ADSCs) after myocardial infarction and compare 2 types of administration pathways. METHODS AND RESULTS: ADSCs from 28 pigs were labeled by transfection. Animals that survived myocardial infarction (n = 19) received: intracoronary culture media (n = 4); intracoronary ADSCs (n = 5); transendocardial culture media (n = 4); or transendocardial ADSCs (n = 6). At 3 weeks' follow-up, intracoronary and transendocardial administration of ADSCs resulted in similar rates of engrafted cells (0.85 [0.19-1.97] versus 2 [1-2] labeled cells/cm(2), respectively; P = NS) and some of those cells expressed smooth muscle cell markers. The intracoronary administration of ADSCs was more effective in increasing the number of small vessels than transendocardial administration (223 +/- 40 versus 168 +/- 35 vessels/mm(2); P < .05). Ejection fraction was not modified by stem cell therapy. CONCLUSIONS: This is the first study to compare intracoronary and transendocardial administration of autologous ADSCs in a porcine model of myocardial infarction. Both pathways of ADSCs delivery are feasible, producing a similar number of engrafted and differentiated cells, although intracoronary administration was more effective in increasing neovascularization.