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Guo Z, Xie J, Wan Y, Zhang M, Qiao L, Yu J, Chen S, Li B, Yao Y. A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis. Open Life Sci 2022; 17:1600-1611. [PMID: 36561500 PMCID: PMC9743193 DOI: 10.1515/biol-2022-0517] [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: 06/23/2022] [Revised: 09/07/2022] [Accepted: 09/24/2022] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modalities such as mammography, computerized tomography, magnetic resonance imaging, ultrasound, and histological imaging. CAD and breast-imaging specialists are in high demand for early detection and diagnosis. This system has the potential to enhance the partiality of traditional histopathological image analysis. This review aims to highlight the recent advancements and the current state of CAD systems for breast cancer detection using different modalities.
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Affiliation(s)
- Zicheng Guo
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Jiping Xie
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Yi Wan
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Min Zhang
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Liang Qiao
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Jiaxuan Yu
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Sijing Chen
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Bingxin Li
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Yongqiang Yao
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
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2
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Thani I, Kasbe T. Expert system based on fuzzy rules for diagnosing breast cancer. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00643-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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3
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Kaushik AC, Sahi S, Wei DQ. Computational Methods for Structure-Based Drug Design Through System Biology. Methods Mol Biol 2022; 2385:161-174. [PMID: 34888721 DOI: 10.1007/978-1-0716-1767-0_9] [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] [Indexed: 06/13/2023]
Abstract
The advances in computational chemistry and biology, computer science, structural biology, and molecular biology go in parallel with the rapid progress in target-based systems. This technique has become a powerful tool in medicinal chemistry for the identification of hit molecules. The recent developments in target-based systems have played a major role in the creation of libraries of compounds, and it has also been widely applied for the design of molecular docking methods. The main advantage of this method is that it hits the fragment that has the strongest binding, has relatively small size, and leads to better compounds in terms of pharmacokinetic properties when compared with virtual screening (VS) and high-throughput screening (HTS) hits. De novo design is an essential aspect of target-based systems and requires the synthesis of chemical to allow the design of promising compound.
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Affiliation(s)
| | - Shakti Sahi
- School of Biotechnology, Gautam Buddha University, Greater Noida, India
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
- Peng Cheng Laboratory, Shenzhen, Guangdong, People's Republic of China.
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4
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Shariaty F, Orooji M, Velichko EN, Zavjalov SV. Texture appearance model, a new model-based segmentation paradigm, application on the segmentation of lung nodule in the CT scan of the chest. Comput Biol Med 2022; 140:105086. [PMID: 34861641 DOI: 10.1016/j.compbiomed.2021.105086] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/24/2021] [Accepted: 11/25/2021] [Indexed: 11/03/2022]
Abstract
Lung cancer causes more than one million deaths worldwide each year. Averages of 5-year survival rate of patients with Non-small cell lung cancer (NSCLC), which is the most common type of lung cancer, is 15%. Computer-Aided Detection (CAD) is a very important tool for identifying lung lesions in medical imaging. In general, the process line of a CAD system can be divided into four main stages: preprocessing, localization, feature extraction, and classification. As segmentation is required for localization in computer vision and medical image analysis, this step has become a major and challenging problem, and much research has been done on new segmentation techniques. In recent years, interest in model-based segmentation methods has increased, and the reason for this is even if some object information is lost, such gaps can be filled by using the previous information in the model. This paper proposed Texture Appearance Model (TAM), which is a new model-based method and segments all types of nodule areas accurately and efficiently, including juxta-pleural nodules, without separating the lung from the surrounding area in a CT scan of the lung. In this method, Texture Representation of Image (TRI) is obtained using tissue texture feature extraction and feature selection algorithms. The proposed method has been evaluated in 85 nodules of the dataset, received from the Iranian hospital, in which the ground-truth annotation by physicians and CT imaging data were provided. The results show that the proposed algorithm has an encouraging performance for distinguishing different types of nodules, including pleural, cavity and non-solid nodules, achieving an average dice similarity coefficient (DSC) of 84.75%.
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Affiliation(s)
- Faridoddin Shariaty
- Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia.
| | - Mahdi Orooji
- Department of Electrical and Computer Engineering, University of California, Davis, United States
| | - Elena N Velichko
- Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia
| | - Sergey V Zavjalov
- Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia
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5
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Makrogiannis S, Zheng K, Harris C. Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms. Front Oncol 2021; 11:725320. [PMID: 35036353 PMCID: PMC8755640 DOI: 10.3389/fonc.2021.725320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
The most common form of cancer among women in both developed and developing countries is breast cancer. The early detection and diagnosis of this disease is significant because it may reduce the number of deaths caused by breast cancer and improve the quality of life of those effected. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) methods have shown promise in recent years for aiding in the human expert reading analysis and improving the accuracy and reproducibility of pathology results. One significant application of CADe and CADx is for breast cancer screening using mammograms. In image processing and machine learning research, relevant results have been produced by sparse analysis methods to represent and recognize imaging patterns. However, application of sparse analysis techniques to the biomedical field is challenging, as the objects of interest may be obscured because of contrast limitations or background tissues, and their appearance may change because of anatomical variability. We introduce methods for label-specific and label-consistent dictionary learning to improve the separation of benign breast masses from malignant breast masses in mammograms. We integrated these approaches into our Spatially Localized Ensemble Sparse Analysis (SLESA) methodology. We performed 10- and 30-fold cross validation (CV) experiments on multiple mammography datasets to measure the classification performance of our methodology and compared it to deep learning models and conventional sparse representation. Results from these experiments show the potential of this methodology for separation of malignant from benign masses as a part of a breast cancer screening workflow.
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Affiliation(s)
- Sokratis Makrogiannis
- Math Imaging and Visual Computing Lab, Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, Dover, DE, United States
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Wang SH, Satapathy SC, Zhou Q, Zhang X, Zhang YD. Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder. JOURNAL OF GRID COMPUTING 2021; 20:1. [PMID: 34931118 PMCID: PMC8674408 DOI: 10.1007/s10723-021-09596-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 11/28/2021] [Indexed: 05/26/2023]
Abstract
Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% ± 1.47%, a specificity of 93.13% ± 0.95%, a precision of 93.15% ± 0.89%, an accuracy of 93.23% ± 0.81%, and an F1 score of 93.23% ± 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches.
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Affiliation(s)
- Shui-Hua Wang
- School of Mathematics and Actuarial Science, University of Leicester, Leicester, LE1 7RH UK
| | | | - Qinghua Zhou
- School of Informatics, University of Leicester, Leicester, LE1 7RH UK
| | - Xin Zhang
- Department of Medical Imaging, The Fourth People’s Hospital of Huai’an, Huai’an, 223002 Jiangsu Province China
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH UK
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7
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Zhang YD, Attique Khan M, Zhu Z, Wang SH. Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis. COMPUTERS, MATERIALS & CONTINUA 2021; 69:3145-3162. [DOI: 10.32604/cmc.2021.018040] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 04/07/2021] [Indexed: 08/25/2024]
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8
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Bouchebbah F, Slimani H. Levels Propagation Approach to Image Segmentation: Application to Breast MR Images. J Digit Imaging 2020; 32:433-449. [PMID: 30706211 DOI: 10.1007/s10278-018-00171-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
Abstract
Accurate segmentation of a breast tumor region is fundamental for treatment. Magnetic resonance imaging (MRI) is a widely used diagnostic tool. In this paper, a new semi-automatic segmentation approach for MRI breast tumor segmentation called Levels Propagation Approach (LPA) is introduced. The introduced segmentation approach takes inspiration from tumor propagation and relies on a finite set of nested and non-overlapped levels. LPA has several features: it is highly suitable to parallelization and offers a simple and dynamic possibility to automate the threshold selection. Furthermore, it allows stopping of the segmentation at any desired limit. Particularly, it allows to avoid to reach the breast skin-line region which is known as a significant issue that reduces the precision and the effectiveness of the breast tumor segmentation. The proposed approach have been tested on two clinical datasets, namely RIDER breast tumor dataset and CMH-LIMED breast tumor dataset. The experimental evaluations have shown that LPA has produced competitive results to some state-of-the-art methods and has acceptable computation complexity.
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Affiliation(s)
- Fatah Bouchebbah
- LIMED Laboratory, Computer Science Department, University of Bejaia, 06000, Bejaia, Algeria.
| | - Hachem Slimani
- LIMED Laboratory, Computer Science Department, University of Bejaia, 06000, Bejaia, Algeria
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9
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Singh SP, Wang L, Gupta S, Goli H, Padmanabhan P, Gulyás B. 3D Deep Learning on Medical Images: A Review. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5097. [PMID: 32906819 PMCID: PMC7570704 DOI: 10.3390/s20185097] [Citation(s) in RCA: 158] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/31/2020] [Accepted: 09/03/2020] [Indexed: 12/20/2022]
Abstract
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.
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Affiliation(s)
- Satya P. Singh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 608232, Singapore; (S.P.S.); (B.G.)
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore
| | - Lipo Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;
| | - Sukrit Gupta
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore; (S.G.); (H.G.)
| | - Haveesh Goli
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore; (S.G.); (H.G.)
| | - Parasuraman Padmanabhan
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 608232, Singapore; (S.P.S.); (B.G.)
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore
| | - Balázs Gulyás
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 608232, Singapore; (S.P.S.); (B.G.)
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore
- Department of Clinical Neuroscience, Karolinska Institute, 17176 Stockholm, Sweden
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10
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Zheng K, Harris C, Bakic P, Makrogiannis S. Spatially localized sparse representations for breast lesion characterization. Comput Biol Med 2020; 123:103914. [PMID: 32768050 PMCID: PMC7416513 DOI: 10.1016/j.compbiomed.2020.103914] [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: 03/27/2020] [Revised: 07/02/2020] [Accepted: 07/11/2020] [Indexed: 11/19/2022]
Abstract
RATIONALE The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of radiological imaging patterns of breast lesions into benign and malignant states. METHODS We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of classifiers (CL) that we expect to yield more accurate numerical solutions than conventional whole-region of interest (ROI) sparse analyses. We introduce two classification decision strategies based on maximum a posteriori probability (BBMAP-S), or a log likelihood function (BBLL-S). RESULTS To evaluate the performance of the proposed approach we used cross-validation techniques on imaging datasets with disease class labels. We utilized the proposed approach for separation of breast lesions into benign and malignant categories in mammograms. The level of difficulty is high in this application and the accuracy may depend on the lesion size. Our results indicate that the proposed integrative sparse analysis addresses the ill-posedness of the approximation problem, producing AUC (area under the receiver operating curve) value of 89.1% for randomized 30-fold cross-validation. CONCLUSIONS Furthermore, our comparative experiments showed that the BBLL-S decision function may yield more accurate classification than BBMAP-S because BBLL-S accounts for possible estimation bias.
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Affiliation(s)
- Keni Zheng
- Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, 1200 N. DuPont Hwy, Dover, DE, 19901-2277, USA
| | - Chelsea Harris
- Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, 1200 N. DuPont Hwy, Dover, DE, 19901-2277, USA
| | - Predrag Bakic
- Department of Radiology, Univ. of Pennsylvania, Philadelphia, PA, 19152, USA
| | - Sokratis Makrogiannis
- Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, 1200 N. DuPont Hwy, Dover, DE, 19901-2277, USA.
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11
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Multi-mass breast cancer classification based on hybrid descriptors and memetic meta-heuristic learning. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-3103-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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12
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Geras KJ, Mann RM, Moy L. Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives. Radiology 2019; 293:246-259. [PMID: 31549948 DOI: 10.1148/radiol.2019182627] [Citation(s) in RCA: 149] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence-based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images.
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Affiliation(s)
- Krzysztof J Geras
- From the Center for Biomedical Imaging (K.J.G., L.M.), Center for Data Science (K.J.G.), Center for Advanced Imaging Innovation and Research (L.M.), and Laura and Isaac Perlmutter Cancer Center (L.M.), New York University School of Medicine, 160 E 34th St, 3rd Floor, New York, NY 10016; Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.)
| | - Ritse M Mann
- From the Center for Biomedical Imaging (K.J.G., L.M.), Center for Data Science (K.J.G.), Center for Advanced Imaging Innovation and Research (L.M.), and Laura and Isaac Perlmutter Cancer Center (L.M.), New York University School of Medicine, 160 E 34th St, 3rd Floor, New York, NY 10016; Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.)
| | - Linda Moy
- From the Center for Biomedical Imaging (K.J.G., L.M.), Center for Data Science (K.J.G.), Center for Advanced Imaging Innovation and Research (L.M.), and Laura and Isaac Perlmutter Cancer Center (L.M.), New York University School of Medicine, 160 E 34th St, 3rd Floor, New York, NY 10016; Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.)
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Zhang Z, Sejdić E. Radiological images and machine learning: Trends, perspectives, and prospects. Comput Biol Med 2019; 108:354-370. [PMID: 31054502 PMCID: PMC6531364 DOI: 10.1016/j.compbiomed.2019.02.017] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 02/19/2019] [Accepted: 02/19/2019] [Indexed: 01/18/2023]
Abstract
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.
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Affiliation(s)
- Zhenwei Zhang
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
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14
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Kaushik AC, Kumar A, Yu CY, Kuo SW, Liang SS, Singh SP, Wang X, Wang YJ, Yen CK, Dai X, Wei DQ, Pan CT, Shiue YL. PCL–DOX microdroplets: an evaluation of the enhanced intracellular delivery of doxorubicin in metastatic cancer cells via in silico and in vitro approaches. NEW J CHEM 2019. [DOI: 10.1039/c9nj01902b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
A schematic diagram of HCC & TACE; injections of HepaSphere with DOX are made into the femoral artery, abdominal aorta, and hepatic artery to make the tumor shrink to a resectable size due to a shortage of nutrients and drug treatment.
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15
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Abstract
This publication presents a computer method for segmenting microcalcifications in mammograms. It makes use of morphological transformations and is composed of two parts. The first part detects microcalcifications morphologically, thus allowing the approximate area of their occurrence to be determined, the contrast to be improved, and noise to be reduced in the mammograms. In the second part, a watershed segmentation of microcalcifications is carried out. This study was carried out on a test set containing 200 ROIs 512 × 512 pixels in size, taken from mammograms from the Digital Database for Screening Mammography (DDSM), including 100 cases showing malignant lesions and 100 cases showing benign ones. The experiments carried out yielded the following average values of the measured indices: 80.5% (similarity index), 75.7% (overlap fraction), 70.8% (overlap value), and 19.8% (extra fraction). The average time of executing all steps of the methods used for a single ROI amounted to 0.83 s.
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Affiliation(s)
- Marcin Ciecholewski
- Faculty of Mathematics and Computer Science, Jagiellonian University, ul. Łojasiewicza 6, 30-348, Kraków, Poland.
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16
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Yassin NIR, Omran S, El Houby EMF, Allam H. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 156:25-45. [PMID: 29428074 DOI: 10.1016/j.cmpb.2017.12.012] [Citation(s) in RCA: 120] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 11/26/2017] [Accepted: 12/11/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. METHODS The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. RESULTS This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems.
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Affiliation(s)
- Nisreen I R Yassin
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Shaimaa Omran
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Enas M F El Houby
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Hemat Allam
- Anaesthesia & Pain, Medical Division, National Research Centre, Dokki, Cairo 12311, Egypt.
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17
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Singh SP, Urooj S. Accurate and Fast Computation of Exponent Fourier Moment. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2017. [DOI: 10.1007/s13369-017-2465-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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18
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DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features. COMPUTERS 2016. [DOI: 10.3390/computers5040028] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Wang Q, Kang W, Hu H, Wang B. HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images. J Med Syst 2016; 40:176. [PMID: 27277277 DOI: 10.1007/s10916-016-0535-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 06/01/2016] [Indexed: 11/24/2022]
Abstract
An Active Appearance Model (AAM) is a computer vision model which can be used to effectively segment lung fields in CT images. However, the fitting result is often inadequate when the lungs are affected by high-density pathologies. To overcome this problem, we propose a Higher-order Singular Value Decomposition (HOSVD)-based Three-dimensional (3D) AAM. An evaluation was performed on 310 diseased lungs form the Lung Image Database Consortium Image Collection. Other contemporary AAMs operate directly on patterns represented by vectors, i.e., before applying the AAM to a 3D lung volume,it has to be vectorized first into a vector pattern by some technique like concatenation. However, some implicit structural or local contextual information may be lost in this transformation. According to the nature of the 3D lung volume, HOSVD is introduced to represent and process the lung in tensor space. Our method can not only directly operate on the original 3D tensor patterns, but also efficiently reduce the computer memory usage. The evaluation resulted in an average Dice coefficient of 97.0 % ± 0.59 %, a mean absolute surface distance error of 1.0403 ± 0.5716 mm, a mean border positioning errors of 0.9187 ± 0.5381 pixel, and a Hausdorff Distance of 20.4064 ± 4.3855, respectively. Experimental results showed that our methods delivered significant and better segmentation results, compared with the three other model-based lung segmentation approaches, namely 3D Snake, 3D ASM and 3D AAM.
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Affiliation(s)
- Qingzhu Wang
- School of Information Engineering, Northeast Dianli University, Jilin, 132012, China.
| | - Wanjun Kang
- School of Information Engineering, Northeast Dianli University, Jilin, 132012, China
| | - Haihui Hu
- School of Information Engineering, Northeast Dianli University, Jilin, 132012, China
| | - Bin Wang
- Jilin Tumor Hospital, Changchun, China
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Carrillo-de-Gea JM, García-Mateos G, Fernández-Alemán JL, Hernández-Hernández JL. A Computer-Aided Detection System for Digital Chest Radiographs. JOURNAL OF HEALTHCARE ENGINEERING 2016; 2016:8208923. [PMID: 27372536 PMCID: PMC5058572 DOI: 10.1155/2016/8208923] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Accepted: 05/05/2016] [Indexed: 11/20/2022]
Abstract
Computer-aided detection systems aim at the automatic detection of diseases using different medical imaging modalities. In this paper, a novel approach to detecting normality/pathology in digital chest radiographs is proposed. The problem tackled is complicated since it is not focused on particular diseases but anything that differs from what is considered as normality. First, the areas of interest of the chest are found using template matching on the images. Then, a texture descriptor called local binary patterns (LBP) is computed for those areas. After that, LBP histograms are applied in a classifier algorithm, which produces the final normality/pathology decision. Our experimental results show the feasibility of the proposal, with success rates above 87% in the best cases. Moreover, our technique is able to locate the possible areas of pathology in nonnormal radiographs. Strengths and limitations of the proposed approach are described in the Conclusions.
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Affiliation(s)
- Juan Manuel Carrillo-de-Gea
- Computer Science and Systems Department, Faculty of Computer Science, University of Murcia, 30100 Murcia, Spain
| | - Ginés García-Mateos
- Computer Science and Systems Department, Faculty of Computer Science, University of Murcia, 30100 Murcia, Spain
| | - José Luis Fernández-Alemán
- Computer Science and Systems Department, Faculty of Computer Science, University of Murcia, 30100 Murcia, Spain
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