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Wang X, Sun H, Dong Y, Huang J, Bai L, Tang Z, Liu S, Chen S. Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking. Sci Rep 2024; 14:2802. [PMID: 38307903 PMCID: PMC10837443 DOI: 10.1038/s41598-024-53306-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 01/30/2024] [Indexed: 02/04/2024] Open
Abstract
Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.
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Affiliation(s)
- Xichao Wang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Hao Sun
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Yongfei Dong
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Jie Huang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Lu Bai
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China.
| | - Songbai Liu
- Suzhou Key Laboratory of Medical Biotechnology, Suzhou Vocational Health College, Suzhou, 215009, Jiangsu, China.
| | - Suning Chen
- National Clinical Research Center for Hematologic Diseases, The First Affiliated Hospital of Soochow University, Jiangsu Institute of Hematology, Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China.
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Tarquino J, Arabyarmohammadi S, Tejada RE, Madabhushi A, Romero E. Intra-nucleus mosaic pattern (InMop) and whole-cell Haralick combined-descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images. Cytometry A 2023; 103:857-867. [PMID: 37565838 PMCID: PMC10841385 DOI: 10.1002/cyto.a.24785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 07/14/2023] [Accepted: 08/08/2023] [Indexed: 08/12/2023]
Abstract
Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an intra-nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n = 260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database-D2 contains acute myeloid leukemia (AML) blasts (n = 3294) and nonblast (n = 15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (n = 6588) and testing in D1 (n = 260), obtaining a training AUC of 0.991 ± 0.002 and AUC = 0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score.
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Affiliation(s)
- Jonathan Tarquino
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Sara Arabyarmohammadi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Rafael Enrique Tejada
- Department of internal medicine, Hemato-oncology unit, Medicine Faculty, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Atlanta Veterans Medical Center, Atlanta, GA, USA
| | - Eduardo Romero
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
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Weng Y, Shen H, Mei L, Liu L, Yao Y, Li R, Wei S, Yan R, Ruan X, Wang D, Wei Y, Deng Y, Zhou Y, Xiao T, Goda K, Liu S, Zhou F, Lei C. Typing of acute leukemia by intelligent optical time-stretch imaging flow cytometry on a chip. LAB ON A CHIP 2023; 23:1703-1712. [PMID: 36799214 DOI: 10.1039/d2lc01048h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Acute leukemia (AL) is one of the top life-threatening diseases. Accurate typing of AL can significantly improve its prognosis. However, conventional methods for AL typing often require cell staining, which is time-consuming and labor-intensive. Furthermore, their performance is highly limited by the specificity and availability of fluorescent labels, which can hardly meet the requirements of AL typing in clinical settings. Here, we demonstrate AL typing by intelligent optical time-stretch (OTS) imaging flow cytometry on a microfluidic chip. Specifically, we employ OTS microscopy to capture the images of cells in clinical bone marrow samples with a spatial resolution of 780 nm at a high flowing speed of 1 m s-1 in a label-free manner. Then, to show the clinical utility of our method for which the features of clinical samples are diverse, we design and construct a deep convolutional neural network (CNN) to analyze the cellular images and determine the AL type of each sample. We measure 30 clinical samples composed of 7 acute lymphoblastic leukemia (ALL) samples, 17 acute myelogenous leukemia (AML) samples, and 6 samples from healthy donors, resulting in a total of 227 620 images acquired. Results show that our method can distinguish ALL and AML with an accuracy of 95.03%, which, to the best of our knowledge, is a record in label-free AL typing. In addition to AL typing, we believe that the high throughput, high accuracy, and label-free operation of our method make it a potential solution for cell analysis in scientific research and clinical settings.
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Affiliation(s)
- Yueyun Weng
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- The Key Laboratory of Transients in Hydraulic Machinery of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, Wuhan, China
| | - Hui Shen
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Liye Mei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Li Liu
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Yifan Yao
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Rubing Li
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Shubin Wei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Ruopeng Yan
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Xiaolan Ruan
- Department of Hematology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Du Wang
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Yongchang Wei
- Department of Radiation & Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yunjie Deng
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Yuqi Zhou
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Tinghui Xiao
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Keisuke Goda
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- Department of Chemistry, University of Tokyo, Tokyo, Japan
- Department of bioengineering, University of California, Los Angeles, USA
| | - Sheng Liu
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- The Key Laboratory of Transients in Hydraulic Machinery of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, Wuhan, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Cheng Lei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- Department of Chemistry, University of Tokyo, Tokyo, Japan
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Haddadi N, Mirzania M, Ansarihadipour H. Syringic acid Attenuates Oxidative Stress in Plasma and Peripheral Blood Mononuclear Cells of Patients with Acute Myeloid Leukemia. Nutr Cancer 2023; 75:1038-1049. [PMID: 36697381 DOI: 10.1080/01635581.2023.2170432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Syringic acid (SA) is a natural phenolic acid that possesses antioxidant properties. The current study aimed to assess the possible ameliorative effects of SA on oxidative stress in patients with acute myeloid leukemia (AML). Twenty-two healthy donors as well as 22 sex- and age-matched AML patients participated in the study. AML patients were at the time of diagnosis and before remission. The peripheral blood mononuclear cells (PBMCs) and plasma samples were obtained and divided into four groups. The groups include: 1) buffer (B), containing isotonic phosphate buffer saline (100 mM, pH 7.4, 1 hr); 2) OX, containing solution subjected to iron-mediated oxidation (2.7 µM, 1 hr); 3) SA, containing SA solution (10 µM, 1 h) as ROS quencher and 4) SA + OX in which samples were pretreated with 10 µM of SA for 1 h, and then exposed to OX solution (2.7 µM) for 1 h. The results indicated that SA caused a significant increase in the activity of glutathione peroxidase (GPX) in PBMCs. Of note, the treatment of PBMCs and plasma samples of AML patients with SA was able to normalize the altered levels of GPX, superoxide dismutase (SOD), and catalase (CAT). The antioxidant effect of SA was further confirmed by analyzing the total oxidant status, lipid peroxidation, and protein carbonylation in both plasma samples and PBMCs of AML patients. According to the results, it seems that SA has strong protective effects on oxidative stress by elevating the total antioxidant status (TAS) of PBMCs and plasma specimens from AML patients.
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Affiliation(s)
- Naghmeh Haddadi
- Department of Biochemistry and Genetics, School of Medicine, Arak University of Medical Sciences, Arak, Iran
| | - Mehrzad Mirzania
- Department of Internal Medicine, Cancer Research Center Cancer Institute, Imam Khomeini Science, Tehran, Iran
| | - Hadi Ansarihadipour
- Department of Biochemistry and Genetics, School of Medicine, Arak University of Medical Sciences, Arak, Iran
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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
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Monaghan SA, Li JL, Liu YC, Ko MY, Boyiadzis M, Chang TY, Wang YF, Lee CC, Swerdlow SH, Ko BS. A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data. Am J Clin Pathol 2022; 157:546-553. [PMID: 34643210 DOI: 10.1093/ajcp/aqab148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/01/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES Flow cytometry (FC) is critical for the diagnosis and monitoring of hematologic malignancies. Machine learning (ML) methods rapidly classify multidimensional data and should dramatically improve the efficiency of FC data analysis. We aimed to build a model to classify acute leukemias, including acute promyelocytic leukemia (APL), and distinguish them from nonneoplastic cytopenias. We also sought to illustrate a method to identify key FC parameters that contribute to the model's performance. METHODS Using data from 531 patients who underwent evaluation for cytopenias and/or acute leukemia, we developed an ML model to rapidly distinguish among APL, acute myeloid leukemia/not APL, acute lymphoblastic leukemia, and nonneoplastic cytopenias. Unsupervised learning using gaussian mixture model and Fisher kernel methods were applied to FC listmode data, followed by supervised support vector machine classification. RESULTS High accuracy (ACC, 94.2%; area under the curve [AUC], 99.5%) was achieved based on the 37-parameter FC panel. Using only 3 parameters, however, yielded similar performance (ACC, 91.7%; AUC, 98.3%) and highlighted the significant contribution of light scatter properties. CONCLUSIONS Our findings underscore the potential for ML to automatically identify and prioritize FC specimens that have critical results, including APL and other acute leukemias.
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Affiliation(s)
- Sara A Monaghan
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC Presbyterian, Pittsburgh, PA, USA
| | - Jeng-Lin Li
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Yen-Chun Liu
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Pathology, St Jude Children’s Research Hospital, Memphis, TN, USA
| | - Ming-Ya Ko
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Michael Boyiadzis
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC Hillman Cancer Center, Pittsburgh, PA, USA
| | | | | | - Chi-Chun Lee
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Steven H Swerdlow
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC Presbyterian, Pittsburgh, PA, USA
| | - Bor-Sheng Ko
- Department of Hematological Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
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7
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Sheikh IM, Chachoo MA. An enforced block diagonal low-rank representation method for the classification of medical image patterns. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2022; 14:1221-1228. [PMID: 35075441 PMCID: PMC8769777 DOI: 10.1007/s41870-021-00841-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/18/2021] [Indexed: 06/14/2023]
Abstract
Low-rank representation based methods have been used on a variety of medical imaging databases for the segmentation and classification of biomedical images. The subspace segmentation of the data is performed by generating the block diagonal coefficient matrix. Whereas, the data is classified by performing the partitioning of the low-rank representation matrix. There exist several such methods for analysing medical images. The major difference between them lies in the construction of the data dictionary. Most of the time, the input data pattern is used as the dictionary for learning the representation matrix. The direct use of the input data for learning the representation degrades the performance of the model because medical images are subjected to outliers of multiple types, which include environmental lighting, image appearance and varying illumination. These types of errors induce noise in the data. It has been observed that the representation-based model is robust when the training data is clean. If the training data contains corrupted subsamples, the performance of the model drops down. We have addressed the mentioned problem by adopting a class-wise dictionary learning approach. In which the pattern of each class is learnt as the set of tuples in the dictionary. The model has been evaluated on several medical imaging datasets, which includes the Break-his dataset, ALL-IDB, biomedical images, covid CT and chest X-ray. The classification performance of the model is best for the biomedical database (99.16%) followed by the Covid dataset (94%), ALL-IDB database (93.47%) and Break-his dataset (93%).
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Affiliation(s)
- Ishfaq Majeed Sheikh
- Department of Computer Science, University of Kashmir, Hazratbal Srinagar, Naseem Bagh, Srinagar, 190006 India
| | - Manzoor Ahmad Chachoo
- Department of Computer Science, University of Kashmir, Hazratbal Srinagar, Naseem Bagh, Srinagar, 190006 India
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8
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Wang CW, Huang SC, Lee YC, Shen YJ, Meng SI, Gaol JL. Deep learning for bone marrow cell detection and classification on whole-slide images. Med Image Anal 2021; 75:102270. [PMID: 34710655 DOI: 10.1016/j.media.2021.102270] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/06/2021] [Accepted: 10/13/2021] [Indexed: 12/19/2022]
Abstract
Bone marrow (BM) examination is an essential step in both diagnosing and managing numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of BM examination, holds the most fundamental and crucial information. However, there are many challenges to perform automated BM NDC analysis on whole-slide images (WSIs), including large dimensions of data to process, complicated cell types with subtle differences. To the authors best knowledge, this is the first study on fully automatic BM NDC using WSIs with 40x objective magnification, which can replace traditional manual counting relying on light microscopy via oil-immersion 100x objective lens with a total 1000x magnification. In this study, we develop an efficient and fully automatic hierarchical deep learning framework for BM NDC WSI analysis in seconds. The proposed hierarchical framework consists of (1) a deep learning model for rapid localization of BM particles and cellular trails generating regions of interest (ROI) for further analysis, (2) a patch-based deep learning model for cell identification of 16 cell types, including megakaryocytes, mitotic cells, and four stages of erythroblasts which have not been demonstrated in previous studies before, and (3) a fast stitching model for integrating patch-based results and producing final outputs. In evaluation, the proposed method is firstly tested on a dataset with a total of 12,426 annotated cells using cross validation, achieving high recall and accuracy of 0.905 ± 0.078 and 0.989 ± 0.006, respectively, and taking only 44 seconds to perform BM NDC analysis for a WSI. To further examine the generalizability of our model, we conduct an evaluation on the second independent dataset with a total of 3005 cells, and the results show that the proposed method also obtains high recall and accuracy of 0.842 and 0.988, respectively. In comparison with the existing small-image-based benchmark methods, the proposed method demonstrates superior performance in recall, accuracy and computational time.
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Affiliation(s)
- Ching-Wei Wang
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan; Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, 106, Taiwan.
| | - Sheng-Chuan Huang
- Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, 100, Taiwan; Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Department of Clinical Pathology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Yu-Ching Lee
- Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, 106, Taiwan
| | - Yu-Jie Shen
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan
| | - Shwu-Ing Meng
- Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, 100, Taiwan
| | - Jeff L Gaol
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan
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Nikitaev VG, Tupitsyn NN, Pronichev AN, Polyakov EV, Dmitrieva VV, Chernysheva OA, Serebryakova IN, Palladina AD. Artificial Intelligence Technologies in the Diagnosis of Acute Lymphoblastic Leukemia and Minimal Residual Disease. BIOMEDICAL ENGINEERING 2021. [DOI: 10.1007/s10527-021-10038-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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10
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Localization and recognition of leukocytes in peripheral blood: A deep learning approach. Comput Biol Med 2020; 126:104034. [DOI: 10.1016/j.compbiomed.2020.104034] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 01/26/2023]
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11
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Anilkumar K, Manoj V, Sagi T. A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.08.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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12
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El Achi H, Khoury JD. Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology. Cancers (Basel) 2020; 12:cancers12040797. [PMID: 32224980 PMCID: PMC7226574 DOI: 10.3390/cancers12040797] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 03/20/2020] [Accepted: 03/24/2020] [Indexed: 12/15/2022] Open
Abstract
Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.
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Affiliation(s)
- Hanadi El Achi
- Department of Pathology and Laboratory Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA;
| | - Joseph D. Khoury
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Correspondence:
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13
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Chandradevan R, Aljudi AA, Drumheller BR, Kunananthaseelan N, Amgad M, Gutman DA, Cooper LAD, Jaye DL. Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. J Transl Med 2020; 100:98-109. [PMID: 31570774 PMCID: PMC6920560 DOI: 10.1038/s41374-019-0325-7] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 07/30/2019] [Accepted: 09/02/2019] [Indexed: 12/16/2022] Open
Abstract
Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable automated counter has yet to be developed, largely due to the inherent complexity of bone marrow specimens. Digital pathology imaging coupled with machine learning algorithms represents a highly promising emerging technology for this purpose. Yet, training datasets for BMA cellular constituents, critical for building and validating machine learning algorithms, are lacking. Herein, we report our experience creating and employing such datasets to develop a machine learning algorithm to detect and classify BMA cells. Utilizing a web-based system that we developed for annotating and managing digital pathology images, over 10,000 cells from scanned whole slide images of BMA smears were manually annotated, including all classes that comprise the standard clinical DCC. We implemented a two-stage, detection and classification approach that allows design flexibility and improved classification accuracy. In a sixfold cross-validation, our algorithms achieved high overall accuracy in detection (0.959 ± 0.008 precision-recall AUC) and classification (0.982 ± 0.03 ROC AUC) using nonneoplastic samples. Testing on a small set of acute myeloid leukemia and multiple myeloma samples demonstrated similar detection and classification performance. In summary, our algorithms showed promising early results and represent an important initial step in the effort to devise a reliable, objective method to automate DCCs. With further development to include formal clinical validation, such a system has the potential to assist in disease diagnosis and prognosis, and significantly impact clinical practice.
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Affiliation(s)
| | - Ahmed A Aljudi
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
- Department of Pathology, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Bradley R Drumheller
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | | | - Mohamed Amgad
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
| | - David A Gutman
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Lee A D Cooper
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA.
- Department of Pathology, Northwestern University, Chicago, IL and Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL, USA.
| | - David L Jaye
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.
- Winship Cancer Institute, Emory University, Atlanta, GA, USA.
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14
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Hegde RB, Prasad K, Hebbar H, Singh BMK, Sandhya I. Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images. J Digit Imaging 2019; 33:361-374. [PMID: 31728805 DOI: 10.1007/s10278-019-00288-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Peripheral blood smear analysis plays a vital role in diagnosing many diseases including cancer. Leukemia is a type of cancer which begins in bone marrow and results in increased number of white blood cells in peripheral blood. Unusual variations in appearance of white blood cells indicate leukemia. In this paper, an automated method for detection of leukemia using image processing approach is proposed. In the present study, 1159 images of different brightness levels and color shades were acquired from Leishman stained peripheral blood smears. SVM classifier was used for classification of white blood cells into normal and abnormal, and also for detection of leukemic WBCs from the abnormal class. Classification of the normal white blood cells into five sub-types was performed using NN classifier. Overall classification accuracy of 98.8% was obtained using the combination of NN and SVM.
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Affiliation(s)
- Roopa B Hegde
- Manipal School of Information Sciences, MAHE, Manipal, 576104, India. .,Department of ECE, NMAM Institute of Technology (Visvesvaraya Technological Univerity, Belagavi), Nitte, Karnataka, 574110, India.
| | - Keerthana Prasad
- Manipal School of Information Sciences, MAHE, Manipal, 576104, India
| | | | - Brij Mohan Kumar Singh
- Department of Immunohematology and Blood Transfusion KMC, MAHE, Manipal, Karnataka, 576104, India
| | - I Sandhya
- Department of Pathology, A J Institute of Medical Sciences and Research Center, Kuntikana, Mangalore, 575004, India
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15
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Salah HT, Muhsen IN, Salama ME, Owaidah T, Hashmi SK. Machine learning applications in the diagnosis of leukemia: Current trends and future directions. Int J Lab Hematol 2019; 41:717-725. [PMID: 31498973 DOI: 10.1111/ijlh.13089] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Revised: 06/27/2019] [Accepted: 07/11/2019] [Indexed: 01/08/2023]
Abstract
Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real-world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically.
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Affiliation(s)
- Haneen T Salah
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Ibrahim N Muhsen
- Department of Medicine, Houston Methodist Hospital, Houston, TX, USA
| | - Mohamed E Salama
- Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN, USA
| | - Tarek Owaidah
- Department of Pathology and Laboratory Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Shahrukh K Hashmi
- Oncology Center, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.,Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
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16
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Bone Marrow Cells Detection: A Technique for the Microscopic Image Analysis. J Med Syst 2019; 43:82. [PMID: 30798374 DOI: 10.1007/s10916-019-1185-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 01/30/2019] [Indexed: 10/27/2022]
Abstract
In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization, segmentation and classification. The localization of BMC is achieved from a color transformation enhanced BMC sample image and stepwise averaging method. In the nucleus segmentation, both stepwise averaging method and Otsu's method are applied to obtain a weighted threshold for segmenting the patch into nucleus and non-nucleus. In the cytoplasm segmentation, a color weakening transformation, an improved region growing method and the K-Means algorithm are employed. The connected cells with BMC will be separated by the marker-controlled watershed algorithm. The features will be extracted for the classification after the segmentation. In this study, the BMC are classified using the support vector machine into five classes; namely, neutrophilic split granulocyte, neutrophilic stab granulocyte, metarubricyte, mature lymphocytes and the outlier (all other cells not listed). Experimental results show that the proposed method achieves superior segmentation and classification performance with an average segmentation accuracy of 91.76% and an average recall rate of 87.49%. The comparison shows that the proposed segmentation and classification methods outperform the existing methods.
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17
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Win KY, Choomchuay S, Hamamoto K, Raveesunthornkiat M. Comparative Study on Automated Cell Nuclei Segmentation Methods for Cytology Pleural Effusion Images. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:9240389. [PMID: 30344991 PMCID: PMC6164204 DOI: 10.1155/2018/9240389] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 07/18/2018] [Indexed: 01/04/2023]
Abstract
Automated cell nuclei segmentation is the most crucial step toward the implementation of a computer-aided diagnosis system for cancer cells. Studies on the automated analysis of cytology pleural effusion images are few because of the lack of reliable cell nuclei segmentation methods. Therefore, this paper presents a comparative study of twelve nuclei segmentation methods for cytology pleural effusion images. Each method involves three main steps: preprocessing, segmentation, and postprocessing. The preprocessing and segmentation stages help enhancing the image quality and extracting the nuclei regions from the rest of the image, respectively. The postprocessing stage helps in refining the segmented nuclei and removing false findings. The segmentation methods are quantitatively evaluated for 35 cytology images of pleural effusion by computing five performance metrics. The evaluation results show that the segmentation performances of the Otsu, k-means, mean shift, Chan-Vese, and graph cut methods are 94, 94, 95, 94, and 93%, respectively, with high abnormal nuclei detection rates. The average computational times per image are 1.08, 36.62, 50.18, 330, and 44.03 seconds, respectively. The findings of this study will be useful for current and potential future studies on cytology images of pleural effusion.
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Affiliation(s)
- Khin Yadanar Win
- Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Somsak Choomchuay
- Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
| | - Kazuhiko Hamamoto
- School of Information and Telecommunication Engineering, Tokai University, Tokyo, Japan
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18
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Kozlowski C, Brumm J, Cain G. An Automated Image Analysis Method to Quantify Veterinary Bone Marrow Cellularity on H&E Sections. Toxicol Pathol 2018; 46:324-335. [DOI: 10.1177/0192623318766457] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Bone marrow toxicity is a common finding when assessing safety of drug candidate molecules. Standard hematoxylin and eosin (H&E) marrow tissue sections are typically manually evaluated to provide a semiquantitative assessment of overall cellularity. Here, we developed an automated image analysis method that allows quantitative assessment of changes in bone marrow cell population in sternal bone. In order to test whether the method was repeatable and sensitive, we compared the automated method with manual subjective histopathology scoring of total cellularity in rat sternal bone marrow samples across 17 independently run studies. The automated method was consistent with manual scoring methodology for detecting altered bone marrow cellularity and, in multiple cases, identified changes at lower doses. The image analysis method allows rapid and more quantitative assessment of bone marrow toxicity compared to manual examination of H&E slides, making it an excellent tool to aid detection of bone marrow cell depletion in preclinical toxicologic studies.
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Affiliation(s)
- Cleopatra Kozlowski
- Safety Assessment Pathology, Genentech Inc., South San Francisco, California, USA
| | - Jochen Brumm
- Biostatistics, Genentech Inc., South San Francisco, California, USA
| | - Gary Cain
- Safety Assessment Pathology, Genentech Inc., South San Francisco, California, USA
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19
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Shafique S, Tehsin S. Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:6125289. [PMID: 29681996 PMCID: PMC5851334 DOI: 10.1155/2018/6125289] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 12/31/2017] [Accepted: 01/31/2018] [Indexed: 11/19/2022]
Abstract
Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.
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Affiliation(s)
- Sarmad Shafique
- Department of Computer Science, Bahria University, Islamabad, Pakistan
| | - Samabia Tehsin
- Department of Computer Science, Bahria University, Islamabad, Pakistan
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20
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Choi JW, Ku Y, Yoo BW, Kim JA, Lee DS, Chai YJ, Kong HJ, Kim HC. White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks. PLoS One 2017; 12:e0189259. [PMID: 29228051 PMCID: PMC5724840 DOI: 10.1371/journal.pone.0189259] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 11/22/2017] [Indexed: 01/12/2023] Open
Abstract
The white blood cell differential count of the bone marrow provides information concerning the distribution of immature and mature cells within maturation stages. The results of such examinations are important for the diagnosis of various diseases and for follow-up care after chemotherapy. However, manual, labor-intensive methods to determine the differential count lead to inter- and intra-variations among the results obtained by hematologists. Therefore, an automated system to conduct the white blood cell differential count is highly desirable, but several difficulties hinder progress. There are variations in the white blood cells of each maturation stage, small inter-class differences within each stage, and variations in images because of the different acquisition and staining processes. Moreover, a large number of classes need to be classified for bone marrow smear analysis, and the high density of touching cells in bone marrow smears renders difficult the segmentation of single cells, which is crucial to traditional image processing and machine learning. Few studies have attempted to discriminate bone marrow cells, and even these have either discriminated only a few classes or yielded insufficient performance. In this study, we propose an automated white blood cell differential counting system from bone marrow smear images using a dual-stage convolutional neural network (CNN). A total of 2,174 patch images were collected for training and testing. The dual-stage CNN classified images into 10 classes of the myeloid and erythroid maturation series, and achieved an accuracy of 97.06%, a precision of 97.13%, a recall of 97.06%, and an F-1 score of 97.1%. The proposed method not only showed high classification performance, but also successfully classified raw images without single cell segmentation and manual feature extraction by implementing CNN. Moreover, it demonstrated rotation and location invariance. These results highlight the promise of the proposed method as an automated white blood cell differential count system.
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Affiliation(s)
- Jin Woo Choi
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
| | - Yunseo Ku
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
| | - Byeong Wook Yoo
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
| | - Jung-Ah Kim
- Department of Laboratory Medicine, Seoul National University College of Medicine, Cancer Research Institute, Seoul, Korea
| | - Dong Soon Lee
- Department of Laboratory Medicine, Seoul National University College of Medicine, Cancer Research Institute, Seoul, Korea
| | - Young Jun Chai
- Department of Surgery, Seoul National University Boramae Medical Center, Seoul, Korea
| | - Hyoun-Joong Kong
- Department of Biomedical Engineering, Chungnam National University College of Medicine, Daejeon, Korea
| | - Hee Chan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
- Department of Biomedical Engineering, Seoul National University Hospital, Seoul, Korea
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea
- * E-mail:
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21
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Su J, Liu S, Song J. A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 152:115-123. [PMID: 29054251 DOI: 10.1016/j.cmpb.2017.09.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 09/11/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES The diagnosis of acute myeloid leukemia (AML) is purely dependent on counting the percentages of blasts (>20%) in the peripheral blood or bone marrow. Manual microscopic examination of peripheral blood or bone marrow aspirate smears is time consuming and less accurate. The first and very important step in blast recognition is the segmentation of the cells from the background for further cell feature extraction and cell classification. In this paper, we aimed to utilize computer technologies in image analysis and artificial intelligence to develop an automatic program for blast recognition and counting in the aspirate smears. METHODS We proposed a method to analyze the aspirate smear images, which first performs segmentation of the cells by k-means cluster, then builds cell image representing model by HMRF (Hidden-Markov Random Field), estimates model parameters through probability of EM (expectation maximization), carries out convergence iteration until optimal value, and finally achieves second stage refined segmentation. Furthermore, the segmentation results are compared with several other methods using six classes of cells respectively. RESULTS The proposed method was applied to six groups of cells from 61 bone marrow aspirate images, and compared with other algorithms for its performance on the analysis of the whole images, the segmentation of nucleus, and the efficiency of calculation. It showed improved segmentation results in both the cropped images and the whole images, which provide the base for down-stream cell feature extraction and identification. CONCLUSIONS Segmentation of the aspirate smear images using the proposed method helps the analyst in differentiating six groups of cells and in the determination of blasts counting, which will be of great significance for the diagnosis of acute myeloid leukemia.
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Affiliation(s)
- Jie Su
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China.
| | - Shuai Liu
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China
| | - Jinming Song
- Department of Hematopathology and Lab Medicines, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
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Li C, Huang X, Jiang T, Xu N. Full-automatic computer aided system for stem cell clustering using content-based microscopic image analysis. Biocybern Biomed Eng 2017. [DOI: 10.1016/j.bbe.2017.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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23
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Training echo state networks for rotation-invariant bone marrow cell classification. Neural Comput Appl 2016; 28:1277-1292. [PMID: 28706349 PMCID: PMC5486804 DOI: 10.1007/s00521-016-2609-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2016] [Accepted: 09/07/2016] [Indexed: 11/26/2022]
Abstract
The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.
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Reta C, Altamirano L, Gonzalez JA, Diaz-Hernandez R, Peregrina H, Olmos I, Alonso JE, Lobato R. Correction: Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias. PLoS One 2015. [PMID: 26208107 PMCID: PMC4514808 DOI: 10.1371/journal.pone.0134066] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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