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Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation. Cancers (Basel) 2022; 14:cancers14184399. [PMID: 36139559 PMCID: PMC9496881 DOI: 10.3390/cancers14184399] [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] [Received: 07/12/2022] [Revised: 09/04/2022] [Accepted: 09/07/2022] [Indexed: 11/29/2022] Open
Abstract
Simple Summary Segmentation of brain tumor images from magnetic resonance imaging (MRI) is a challenging topic in medical image analysis. The brain tumor can take many shapes, and MRI images vary considerably in intensity, making lesion detection difficult for radiologists. This paper proposes a three-step approach to solving this problem: (1) pre-processing, based on morphological operations, is applied to remove the skull bone from the image; (2) the particle swarm optimization (PSO) algorithm, with a two-way fixed-effects analysis of variance (ANOVA)-based fitness function, is used to find the optimal block containing the brain lesion; (3) the K-means clustering algorithm is adopted, to classify the detected block as tumor or non-tumor. An extensive experimental analysis, including visual and statistical evaluations, was conducted, using two MRI databases: a private database provided by the Kouba imaging center—Algiers (KICA)—and the multimodal brain tumor segmentation challenge (BraTS) 2015 database. The results show that the proposed methodology achieved impressive performance, compared to several competing approaches. Abstract Segmentation of brain tumor images, to refine the detection and understanding of abnormal masses in the brain, is an important research topic in medical imaging. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed, to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied, to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block, to classify it as a tumor or not. A thorough evaluation of the proposed algorithm was performed, using: (1) a private MRI database provided by the Kouba imaging center—Algiers (KICA); (2) the multimodal brain tumor segmentation challenge (BraTS) 2015 database. Estimates of the selected fitness function were first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion, to demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm was then compared to the results of several state-of-the-art techniques. The results obtained, by using the Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements, demonstrated the superiority of the proposed optimized segmentation algorithm over equivalent techniques.
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Wang X, Wang R, Yang S, Zhang J, Wang M, Zhong D, Zhang J, Han X. Combining Radiology and Pathology for Automatic Glioma Classification. Front Bioeng Biotechnol 2022; 10:841958. [PMID: 35387307 PMCID: PMC8977526 DOI: 10.3389/fbioe.2022.841958] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/23/2022] [Indexed: 11/17/2022] Open
Abstract
Subtype classification is critical in the treatment of gliomas because different subtypes lead to different treatment options and postoperative care. Although many radiological- or histological-based glioma classification algorithms have been developed, most of them focus on single-modality data. In this paper, we propose an innovative two-stage model to classify gliomas into three subtypes (i.e., glioblastoma, oligodendroglioma, and astrocytoma) based on radiology and histology data. In the first stage, our model classifies each image as having glioblastoma or not. Based on the obtained non-glioblastoma images, the second stage aims to accurately distinguish astrocytoma and oligodendroglioma. The radiological images and histological images pass through the two-stage design with 3D and 2D models, respectively. Then, an ensemble classification network is designed to automatically integrate the features of the two modalities. We have verified our method by participating in the MICCAI 2020 CPM-RadPath Challenge and won 1st place. Our proposed model achieves high performance on the validation set with a balanced accuracy of 0.889, Cohen’s Kappa of 0.903, and an F1-score of 0.943. Our model could advance multimodal-based glioma research and provide assistance to pathologists and neurologists in diagnosing glioma subtypes. The code has been publicly available online at https://github.com/Xiyue-Wang/1st-in-MICCAI2020-CPM.
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Affiliation(s)
- Xiyue Wang
- College of Biomedical Engineering, Sichuan University, Chengdu, China.,College of Computer Science, Sichuan University, Chengdu, China
| | - Ruijie Wang
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, China
| | | | | | - Minghui Wang
- College of Biomedical Engineering, Sichuan University, Chengdu, China.,College of Computer Science, Sichuan University, Chengdu, China
| | - Dexing Zhong
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, China.,Pazhou Lab, Guangzhou, China.,State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
| | - Jing Zhang
- College of Biomedical Engineering, Sichuan University, Chengdu, China
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Allahverdy A, Zare-Sadeghi A, Kalantari R, Moqadam R, Loghmani N, Shiran M. Brain tumor segmentation using hierarchical combination of fuzzy logic and cellular automata. JOURNAL OF MEDICAL SIGNALS & SENSORS 2022; 12:263-268. [PMID: 36120403 PMCID: PMC9480508 DOI: 10.4103/jmss.jmss_128_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 10/30/2021] [Accepted: 01/01/2022] [Indexed: 11/25/2022]
Abstract
Background: Magnetic resonance (MR) image is one of the most important diagnostic tools for brain tumor detection. Segmentation of glioma tumor region in brain MR images is challenging in medical image processing problems. Precise and reliable segmentation algorithms can be significantly helpful in the diagnosis and treatment planning. Methods: In this article, a novel brain tumor segmentation method is introduced as a postsegmentation module, which uses the primary segmentation method's output as input and makes the segmentation performance values better. This approach is a combination of fuzzy logic and cellular automata (CA). Results: The BraTS online dataset has been used for implementing the proposed method. In the first step, the intensity of each pixel is fed to a fuzzy system to label each pixel, and at the second step, the label of each pixel is fed to a fuzzy CA to make the performance of segmentation better. This step repeated while the performance saturated. The accuracy of the first step was 85.8%, but the accuracy of segmentation after using fuzzy CA was obtained to 99.8%. Conclusion: The practical results have shown that our proposed method could improve the brain tumor segmentation in MR images significantly in comparison with other approaches.
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Ma Y, Wang J, Wu J, Tong C, Zhang T. Meta-analysis of cellular toxicity for graphene via data-mining the literature and machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 793:148532. [PMID: 34328986 DOI: 10.1016/j.scitotenv.2021.148532] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/13/2021] [Accepted: 06/14/2021] [Indexed: 06/13/2023]
Abstract
Since graphene is currently incorporated into various consumer products and used in a variety of applications, determining the relationships between the physicochemical properties of graphene and its toxicity is critical for conducting environmental and health risk analyses. Data from the literature suggest that exposure to graphene may result in cytotoxicity. However, existing graphene toxicity data are complex and heterogeneous, making it difficult to conduct risk assessments. Here, we conducted a meta-analysis of published data on the cytotoxicity of graphene based on 792 publications, including 986 cell viability data points, 762 half maximal inhibitory concentration (IC50) data points, and 100 lactate dehydrogenase (LDH) release data points. Models to predict graphene cytotoxicity were then developed based on cell viability, IC50, and LDH release as toxicity endpoints using random forests learning algorithms. The most influential attributes influencing graphene cytotoxicity were revealed to be exposure dose and detection method for cell viability, diameter and surface modification for IC50, and detection method and organ source for LDH release. The meta-analysis produced three sets of key attributes for the three abovementioned toxicity endpoints that can be used in future studies of graphene toxicity. The findings indicate that rigorous data mining protocols can be combined with suitable machine learning tools to develop models with good predictive power and accuracy. The results also provide guidance for the design of safe graphene materials.
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Affiliation(s)
- Ying Ma
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Jianli Wang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Jingying Wu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Chuxuan Tong
- School of Information Technology and Electrical Engineering, The University of Queensland Brisbane, QLD 4072, Australia
| | - Ting Zhang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
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Shaari H, Kevrić J, Jukić S, Bešić L, Jokić D, Ahmed N, Rajs V. Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: Narrative Review of Techniques and Challenges. Brain Sci 2021; 11:brainsci11060716. [PMID: 34071202 PMCID: PMC8230188 DOI: 10.3390/brainsci11060716] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/10/2021] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
Brain tumors diagnosis in children is a scientific concern due to rapid anatomical, metabolic, and functional changes arising in the brain and non-specific or conflicting imaging results. Pediatric brain tumors diagnosis is typically centralized in clinical practice on the basis of diagnostic clues such as, child age, tumor location and incidence, clinical history, and imaging (Magnetic resonance imaging MRI / computed tomography CT) findings. The implementation of deep learning has rapidly propagated in almost every field in recent years, particularly in the medical images’ evaluation. This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning. The purpose of this review paper is to include a detailed summary by first providing a succinct guide to the types of pediatric brain tumors and pediatric brain tumor imaging techniques. Then, we will present the research carried out by summarizing the scientific contributions to the field of pediatric brain tumor imaging processing and analysis. Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included.
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Affiliation(s)
- Hala Shaari
- Department of Information Technologies, Faculty of Engineering and Natural Sciences, International BURCH University, 71000 Sarajevo, Bosnia and Herzegovina;
| | - Jasmin Kevrić
- Faculty of Engineering and Natural Sciences, International BURCH University, 71000 Sarajevo, Bosnia and Herzegovina; (J.K.); (S.J.); (L.B.); (D.J.)
| | - Samed Jukić
- Faculty of Engineering and Natural Sciences, International BURCH University, 71000 Sarajevo, Bosnia and Herzegovina; (J.K.); (S.J.); (L.B.); (D.J.)
| | - Larisa Bešić
- Faculty of Engineering and Natural Sciences, International BURCH University, 71000 Sarajevo, Bosnia and Herzegovina; (J.K.); (S.J.); (L.B.); (D.J.)
| | - Dejan Jokić
- Faculty of Engineering and Natural Sciences, International BURCH University, 71000 Sarajevo, Bosnia and Herzegovina; (J.K.); (S.J.); (L.B.); (D.J.)
| | - Nuredin Ahmed
- Control Department, Technical Computer College Tripoli, Tripoli 00218, Libya;
| | - Vladimir Rajs
- Department of Power, Electronics and Telecommunication Engineering, Faculty of Technical Science, University of Novi Sad, 21000 Novi Sad, Serbia
- Correspondence:
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Buchlak QD, Esmaili N, Leveque JC, Bennett C, Farrokhi F, Piccardi M. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review. J Clin Neurosci 2021; 89:177-198. [PMID: 34119265 DOI: 10.1016/j.jocn.2021.04.043] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/30/2021] [Indexed: 12/13/2022]
Abstract
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.
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Affiliation(s)
- Quinlan D Buchlak
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia.
| | - Nazanin Esmaili
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia; Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia
| | | | - Christine Bennett
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia
| | - Farrokh Farrokhi
- Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA
| | - Massimo Piccardi
- Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia
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Wady SH, Yousif RZ, Hasan HR. A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8125392. [PMID: 32733955 PMCID: PMC7369660 DOI: 10.1155/2020/8125392] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 04/10/2020] [Accepted: 06/08/2020] [Indexed: 12/28/2022]
Abstract
Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.
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Affiliation(s)
- Shakhawan H. Wady
- Applied Computer, College of Medicals and Applied Sciences, Charmo University, Chamchamal, Sulaimani, KRG, Iraq
- Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, KRG, Iraq
- Department of Information Technology, University College of Goizha, Sulaimani, KRG, Iraq
| | - Raghad Z. Yousif
- Department of Physics, College of Science, Salahaddin University, Erbil, KRG, Iraq
- Department of IT, College of Information Technology, Catholic University in Erbil, KRG, Iraq
| | - Harith R. Hasan
- Department of Computer Science, Kurdistan Technical Institute, Sulaimani, KRG, Iraq
- Computer Science Institute, Sulaimani Polytechnic University, Sulaimani, KRG, Iraq
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A review on brain tumor segmentation of MRI images. Magn Reson Imaging 2019; 61:247-259. [DOI: 10.1016/j.mri.2019.05.043] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 05/30/2019] [Accepted: 05/30/2019] [Indexed: 01/17/2023]
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Das A, Das P, Panda SS, Sabut S. Detection of Liver Cancer Using Modified Fuzzy Clustering and Decision Tree Classifier in CT Images. PATTERN RECOGNITION AND IMAGE ANALYSIS 2019. [DOI: 10.1134/s1054661819020056] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Zhang C, Dai J. An incremental attribute reduction approach based on knowledge granularity for incomplete decision systems. GRANULAR COMPUTING 2019. [DOI: 10.1007/s41066-019-00173-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Das DK, Bose S, Maiti AK, Mitra B, Mukherjee G, Dutta PK. Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis. Tissue Cell 2018; 53:111-119. [DOI: 10.1016/j.tice.2018.06.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 05/29/2018] [Accepted: 06/18/2018] [Indexed: 12/29/2022]
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Saleh E, Błaszczyński J, Moreno A, Valls A, Romero-Aroca P, de la Riva-Fernández S, Słowiński R. Learning ensemble classifiers for diabetic retinopathy assessment. Artif Intell Med 2018; 85:50-63. [DOI: 10.1016/j.artmed.2017.09.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Revised: 08/29/2017] [Accepted: 09/13/2017] [Indexed: 12/27/2022]
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Mohan G, Subashini MM. MRI based medical image analysis: Survey on brain tumor grade classification. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.07.007] [Citation(s) in RCA: 196] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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