1
|
Garcia-Lamont F, Lopez-Chau A, Cervantes J, Ruiz S. Nucleus segmentation of white blood cells in blood smear images by modeling the pixels' intensities as a set of three Gaussian distributions. Med Biol Eng Comput 2024; 62:2371-2388. [PMID: 38584206 DOI: 10.1007/s11517-024-03065-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 02/27/2024] [Indexed: 04/09/2024]
Abstract
The precise segmentation of white blood cells (WBCs) within blood smear images is a significant challenge with implications for both medical research and image processing. Of particular importance is the often neglected task of accurately segmenting WBC nuclei, an aspect that currently lacks dedicated methodologies. This paper introduces a straightforward and efficient method designed to fill this critical gap, providing an effective solution for the efficient segmentation of WBC nuclei. In blood smear imagery, the distinctive coloration of WBCs contrasts with the hues of other blood components. The inherent obscurity of WBCs prompts their segmentation by isolating pixels with minimal intensities. To streamline this process, our proposed method employs the Laplacian pyramid technique to decorrelate pixels in blood smear images, thereby amplifying the contrast. Subsequently, the intensities of pixels constituting blood cells, encompassing WBCs and the background, are modeled using three Gaussian random variables. Capitalizing on this feature, we implement the Gaussian mixture model (GMM) clustering method to determine the optimal threshold value, facilitating a highly precise segmentation of WBC nuclei. The proposed method demonstrates the capability to process images containing a single WBC as well as effectively functioning with images containing multiple cells of this type. Evaluation of the method on the ALL-IDB, ALL-IDB2, CellaVision, and JTSC datasets yielded accuracy values of 0.9802, 0.9725, 0.9772, and 0.9730, respectively. Comparative analysis with state-of-the-art methods revealed a notably comparable performance, underscoring the effectiveness of the proposed approach. The method presented in this article is highly competitive for segmenting the nuclei of WBCs compared to state-of-the-art methods. The three main advantages of our method are its ability to process images containing one or more WBCs, the automatic calculation of threshold values for each processed image, eliminating the need for manual parameter adjustments. Lastly, the method is efficient, as its algorithmic complexity is approximately O ( n m ) .
Collapse
Affiliation(s)
- Farid Garcia-Lamont
- Centro Universitario UAEM Texcoco, Universidad Autonoma del Estado de Mexico, Av. Jardin Zumpango, s/n, Fraccionamiento El Tejocote, Texcoco, CP 56259, Estado de Mexico, Mexico.
| | - Asdrubal Lopez-Chau
- Centro Universitario UAEM Zumpango. Research Laboratory of Engineering and Applied Sciences, Universidad Autonoma del Estado de Mexico, Camino Viejo a Jilotzingo, continuacion Calle Rayon, Zumpango, CP 55600, Estado de Mexico, Mexico.
| | - Jair Cervantes
- Centro Universitario UAEM Texcoco, Universidad Autonoma del Estado de Mexico, Av. Jardin Zumpango, s/n, Fraccionamiento El Tejocote, Texcoco, CP 56259, Estado de Mexico, Mexico
| | - Sergio Ruiz
- Centro Universitario UAEM Texcoco, Universidad Autonoma del Estado de Mexico, Av. Jardin Zumpango, s/n, Fraccionamiento El Tejocote, Texcoco, CP 56259, Estado de Mexico, Mexico
| |
Collapse
|
2
|
Lin Y, Chen Q, Chen T. Recent advancements in machine learning for bone marrow cell morphology analysis. Front Med (Lausanne) 2024; 11:1402768. [PMID: 38947236 PMCID: PMC11211563 DOI: 10.3389/fmed.2024.1402768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 05/31/2024] [Indexed: 07/02/2024] Open
Abstract
As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications. Artificial intelligence serves as a potent tool in supporting the diagnostic process of clinical bone marrow cell morphology. Despite the potential of artificial intelligence to augment clinical diagnostics in this domain, manual analysis of bone marrow cell morphology remains the gold standard and an indispensable tool for identifying, diagnosing, and assessing the efficacy of hematologic disorders. However, the traditional manual approach is not without limitations and shortcomings, necessitating, the exploration of automated solutions for examining and analyzing bone marrow cytomorphology. This review provides a multidimensional account of six bone marrow cell morphology processes: automated bone marrow cell morphology detection, automated bone marrow cell morphology segmentation, automated bone marrow cell morphology identification, automated bone marrow cell morphology classification, automated bone marrow cell morphology enumeration, and automated bone marrow cell morphology diagnosis. Highlighting the attractiveness and potential of machine learning systems based on bone marrow cell morphology, the review synthesizes current research and recent advances in the application of machine learning in this field. The objective of this review is to offer recommendations to hematologists for selecting the most suitable machine learning algorithms to automate bone marrow cell morphology examinations, enabling swift and precise analysis of bone marrow cytopathic trends for early disease identification and diagnosis. Furthermore, the review endeavors to delineate potential future research avenues for machine learning-based applications in bone marrow cell morphology analysis.
Collapse
Affiliation(s)
- Yifei Lin
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
| | - Qingquan Chen
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
- The School of Public Health, Fujian Medical University, Fuzhou, Fujian, China
| | - Tebin Chen
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| |
Collapse
|
3
|
Bermejo-Peláez D, Rueda Charro S, García Roa M, Trelles-Martínez R, Bobes-Fernández A, Hidalgo Soto M, García-Vicente R, Morales ML, Rodríguez-García A, Ortiz-Ruiz A, Blanco Sánchez A, Mousa Urbina A, Álamo E, Lin L, Dacal E, Cuadrado D, Postigo M, Vladimirov A, Garcia-Villena J, Santos A, Ledesma-Carbayo MJ, Ayala R, Martínez-López J, Linares M, Luengo-Oroz M. Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2024; 30:151-159. [PMID: 38302194 DOI: 10.1093/micmic/ozad143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/15/2023] [Accepted: 12/22/2023] [Indexed: 02/03/2024]
Abstract
Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and subject to high observer variability. In this work, we present a comprehensive digital microscopy system that enables BMA analysis for cell type counting and differentiation in an efficient and objective manner. This system not only provides an accessible and simple method to digitize, store, and analyze BMA samples remotely but is also supported by an Artificial Intelligence (AI) pipeline that accelerates the differential cell counting process and reduces interobserver variability. It has been designed to integrate AI algorithms with the daily clinical routine and can be used in any regular hospital workflow.
Collapse
Affiliation(s)
| | | | - María García Roa
- Department of Hematology, Hospital Universitario Fundación Alcorcón, C. Budapest, 1, Alcorcón 28922, Madrid, Spain
| | - Roberto Trelles-Martínez
- Department of Hematology, Hospital Universitario Fundación Alcorcón, C. Budapest, 1, Alcorcón 28922, Madrid, Spain
| | - Alejandro Bobes-Fernández
- Department of Hematology, Hospital Universitario Fundación Alcorcón, C. Budapest, 1, Alcorcón 28922, Madrid, Spain
| | - Marta Hidalgo Soto
- Vall Hebron Institute of Oncology (VHIO), Carrer de Natzaret, 115-117, Horta-Guinardó, Barcelona 08035, Spain
| | - Roberto García-Vicente
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - María Luz Morales
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - Alba Rodríguez-García
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - Alejandra Ortiz-Ruiz
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - Alberto Blanco Sánchez
- Department of Hematology, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | | | - Elisa Álamo
- Spotlab, P.º de Juan XXIII, 36B, Madrid 28040, Spain
| | - Lin Lin
- Spotlab, P.º de Juan XXIII, 36B, Madrid 28040, Spain
- Biomedical Image Technologies Laboratory, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense, 30, Madrid 28040, Spain
| | - Elena Dacal
- Spotlab, P.º de Juan XXIII, 36B, Madrid 28040, Spain
| | | | - María Postigo
- Spotlab, P.º de Juan XXIII, 36B, Madrid 28040, Spain
| | | | | | - Andrés Santos
- Biomedical Image Technologies Laboratory, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense, 30, Madrid 28040, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - María Jesús Ledesma-Carbayo
- Biomedical Image Technologies Laboratory, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense, 30, Madrid 28040, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - Rosa Ayala
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
- Department of Hematology, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Joaquín Martínez-López
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
- Department of Hematology, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - María Linares
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, Pl. de Ramón y Cajal, s/n, Madrid 28040, Spain
| | | |
Collapse
|
4
|
Wang X, Wang Y, Qi C, Qiao S, Yang S, Wang R, Jin H, Zhang J. The Application of Morphogo in the Detection of Megakaryocytes from Bone Marrow Digital Images with Convolutional Neural Networks. Technol Cancer Res Treat 2023; 22:15330338221150069. [PMID: 36700246 PMCID: PMC9896096 DOI: 10.1177/15330338221150069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
The evaluation of megakaryocytes is an important part of the work up on bone marrow smear examination. It has significance in the differential diagnosis, therapeutic efficacy assessment, and predication of prognosis of many hematologic diseases. The process of manual identification of megakaryocytes are tedious and lack of reproducibility; therefore, a reliable method of automated megakaryocytic identification is urgently needed. Three hundred and thirty-three bone marrow aspirate smears were digitized by Morphogo system. Pathologists annotated megakaryocytes on the digital images of marrow smears are applied to construct a large dataset for testing the system's predictive performance. Subsequently, we obtained megakaryocyte count and classification for each sample by different methods (system-automated analysis, system-assisted analysis, and microscopic examination) to study the correlation between different counting and classification methods. Morphogo system localized cells likely to be megakaryocytes on digital smears, which were later annotated by pathologists and the system, respectively. The system showed outstanding performance in identifying megakaryocytes in bone marrow smears with high sensitivity (96.57%) and specificity (89.71%). The overall correlation between the different methods was confirmed the high consistency (r ≥ 0.7218, R2 ≥ 0.5211) with microscopic examination in classifying megakaryocytes. Morphogo system was proved as a reliable screen tool for analyzing megakaryocytes. The application of Morphogo system shows promises to advance the automation and standardization of bone marrow smear examination.
Collapse
Affiliation(s)
- Xiaofen Wang
- Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, Hangzhou, Zhejiang, China,Key Laboratory of Precision Medicine in Diagnosis and Monitoring
Research of Zhejiang Province, China
| | - Ying Wang
- Department of Medical Development, Hangzhou Zhiwei
Information&Technology Ltd., Hangzhou, China
| | - Chao Qi
- Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, Hangzhou, Zhejiang, China,Key Laboratory of Precision Medicine in Diagnosis and Monitoring
Research of Zhejiang Province, China
| | - Sai Qiao
- Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, Hangzhou, Zhejiang, China,Key Laboratory of Precision Medicine in Diagnosis and Monitoring
Research of Zhejiang Province, China
| | - Suwen Yang
- Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, Hangzhou, Zhejiang, China,Key Laboratory of Precision Medicine in Diagnosis and Monitoring
Research of Zhejiang Province, China
| | - Rongrong Wang
- Department of Clinical Pharmacy, the First Affiliated Hospital,
Zhejiang University, Hangzhou, Zhejiang, China
| | - Hong Jin
- Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, Hangzhou, Zhejiang, China,Key Laboratory of Precision Medicine in Diagnosis and Monitoring
Research of Zhejiang Province, China
| | - Jun Zhang
- Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine,
Zhejiang University, Hangzhou, Zhejiang, China,Key Laboratory of Precision Medicine in Diagnosis and Monitoring
Research of Zhejiang Province, China,Jun Zhang, Clinical Laboratory, Sir Run Run
Shaw Hospital, School of Medicine, Zhejiang University, No.3, Qingchun East
Road, Shangcheng District, Hangzhou, Zhejiang 310016, China.
Hong Jin, Clinical Laboratory, Sir
Run Run Shaw Hospital, School of Medicine, Zhejiang University, No.3, Qingchun
East Road, Shangcheng District, Hangzhou, Zhejiang 310016, China.
| |
Collapse
|
5
|
Noshad A, Fallahi S. A new hybrid framework based on deep neural networks and JAYA optimization algorithm for feature selection using SVM applied to classification of acute lymphoblastic Leukaemia. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2157748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Ali Noshad
- Department of Engineering, Polytechnic University of Milan, Milan, Italy
| | - Saeed Fallahi
- Department of Mathematics, Salman Farsi University of Kazerun, Kazerun, Iran
| |
Collapse
|
6
|
Feng Y, Wang Z, Cui R, Xiao M, Gao H, Bai H, Delvoux B, Zhang Z, Dekker A, Romano A, Wang S, Traverso A, Liu C, Zhang Z. Clinical analysis and artificial intelligence survival prediction of serous ovarian cancer based on preoperative circulating leukocytes. J Ovarian Res 2022; 15:64. [PMID: 35610701 PMCID: PMC9129061 DOI: 10.1186/s13048-022-00994-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 05/03/2022] [Indexed: 12/02/2022] Open
Abstract
Circulating leukocytes are an important part of the immune system. The aim of this work is to explore the role of preoperative circulating leukocytes in serous ovarian carcinoma and investigate whether they can be used to predict survival prognosis. Routine blood test results and clinical information of patients with serous ovarian carcinoma were retrospectively collected. And to predict survival according to the blood routine test result the decision tree method was applied to build a machine learning model. The results showed that the number of preoperative white blood cells (p = 0.022), monocytes (p < 0.001), lymphocytes (p < 0.001), neutrophils (p < 0.001), and eosinophils (p < 0.001) and the monocyte to lymphocyte (MO/LY) ratio in the serous ovarian cancer group were significantly different from those in the control group. These factors also showed a correlation with other clinicopathological characteristics. The MO/LY was the root node of the decision tree, and the predictive AUC for survival was 0.69. The features involved in the decision tree were the MO/LY, differentiation status, CA125 level, neutrophils (NE,) ascites cytology, LY% and age. In conclusion, the number and percentage of preoperative leukocytes in patients with ovarian cancer is changed significantly compared to those in the normal control group, as well as the MO/LY. A decision tree was built to predict the survival of patients with serous ovarian cancer based on the CA125 level, white blood cell (WBC) count, presence of lymph node metastasis (LNM), MO count, the MO/LY ratio, differentiation status, stage, LY%, ascites cytology, and age.
Collapse
Affiliation(s)
- Ying Feng
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.,Department of Obstetrics and Gynecology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Zhixiang Wang
- Department of Radiation Oncology (Maastro), GROW-School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ran Cui
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
| | - Meizhu Xiao
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Huiqiao Gao
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Huimin Bai
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Bert Delvoux
- Department of Obstetrics and Gynecology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Zhen Zhang
- Department of Radiation Oncology (Maastro), GROW-School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW-School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andrea Romano
- Department of Obstetrics and Gynecology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Shuzhen Wang
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW-School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
| | - Chongdong Liu
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
| | - Zhenyu Zhang
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
7
|
Garcia-Lamont F, Alvarado M, Cervantes J. Systematic segmentation method based on PCA of image hue features for white blood cell counting. PLoS One 2021; 16:e0261857. [PMID: 34972155 PMCID: PMC8719728 DOI: 10.1371/journal.pone.0261857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/10/2021] [Indexed: 11/25/2022] Open
Abstract
Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works.
Collapse
Affiliation(s)
- Farid Garcia-Lamont
- Universidad Autónoma del Estado de México, Centro Universitario UAEM Texcoco, Texcoco-Estado de México, México
| | - Matias Alvarado
- Centro de Investigación y de Estudios Avanzados del IPN, Departamento de Computación, México city, CDMX-México, México
| | - Jair Cervantes
- Universidad Autónoma del Estado de México, Centro Universitario UAEM Texcoco, Texcoco-Estado de México, México
| |
Collapse
|
8
|
Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study. J Med Syst 2020; 44:184. [PMID: 32894360 PMCID: PMC7476995 DOI: 10.1007/s10916-020-01654-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 08/25/2020] [Indexed: 01/17/2023]
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 < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders.
Collapse
|
9
|
Sudha K, Geetha P. Leukocyte segmentation in peripheral blood images using a novel edge strength cue-based location detection method. Med Biol Eng Comput 2020; 58:1995-2008. [PMID: 32596772 DOI: 10.1007/s11517-020-02204-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 05/22/2020] [Indexed: 11/27/2022]
Abstract
The classification of leukocytes in peripheral blood images is an important milestone to be achieved because it can greatly assist pathologists to diagnose diseases such as leukemia, anemia, and other blood disorders. To a certain extent, a good segmentation method for identifying leukocytes from their background is the first step to the efficient functioning of the leukocytes classification system. However, the morphological structure of leukocytes, poor contrast, and the variations in their shape and size lead to the degradation of the segmentation accuracy. In this paper, we propose a new leukocyte segmentation framework that first locates and then segments leukocytes from peripheral blood images. Here, the locations of the leukocytes are first identified using a novel edge strength cue (ESc), and later, the Grabcut model is deployed to obtain the segmentation of the leukocytes. The novelty lies in the way the location of the leukocytes is detected, and this improves the leukocyte segmentation accuracy. The experimental evaluation is performed on ALL-IDB1, Cellavision, and LISC datasets for leukocyte segmentation based on the detection of the ESc location. Experimental results are evaluated using precision, recall, and F-score measures. The proposed method outperforms the state-of-the-art techniques. Additionally, the computation time of the proposed method is analyzed and presented in the study. Graphical Abstract Leukocytes Location Detection and Segmentation.
Collapse
Affiliation(s)
- K Sudha
- Department of Computer Science and Engineering, College of Engineering, Guindy, Anna University, Chennai, India.
| | - P Geetha
- Department of Computer Science and Engineering, College of Engineering, Guindy, Anna University, Chennai, India
| |
Collapse
|