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Guo L, Shi L, Wang W, Wang X. Neural Network Classification Algorithm Based on Self-attention Mechanism and Ensemble Learning for MASLD Ultrasound Images. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1361-1371. [PMID: 38910034 DOI: 10.1016/j.ultrasmedbio.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 04/11/2024] [Accepted: 05/10/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Ultrasound image examination has become the preferred choice for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD) due to its non-invasive nature. Computer-aided diagnosis (CAD) technology can assist doctors in avoiding deviations in the detection and classification of MASLD. METHOD We propose a hybrid model that integrates the pre-trained VGG16 network with an attention mechanism and a stacking ensemble learning model, which is capable of multi-scale feature aggregation based on the self-attention mechanism and multi-classification model fusion (Logistic regression, random forest, support vector machine) based on stacking ensemble learning. The proposed hybrid method achieves four classifications of normal, mild, moderate, and severe fatty liver based on ultrasound images. RESULT AND CONCLUSION Our proposed hybrid model reaches an accuracy of 91.34% and exhibits superior robustness against interference, which is better than traditional neural network algorithms. Experimental results show that, compared with the pre-trained VGG16 model, adding the self-attention mechanism improves the accuracy by 3.02%. Using the stacking ensemble learning model as a classifier further increases the accuracy to 91.34%, exceeding any single classifier such as LR (89.86%) and SVM (90.34%) and RF (90.73%). The proposed hybrid method can effectively improve the efficiency and accuracy of MASLD ultrasound image detection.
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Affiliation(s)
- Lijuan Guo
- Children's Hospital of Shanxi & Women Health Center of Shanxi, Taiyuan, China.
| | - Liling Shi
- Children's Hospital of Shanxi & Women Health Center of Shanxi, Taiyuan, China.
| | - Wenjuan Wang
- Shanxi International Travel Health Care Center, Taiyuan, China
| | - Xiaotong Wang
- Children's Hospital of Shanxi & Women Health Center of Shanxi, Taiyuan, China
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2
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Qi Y, Vianna P, Cadrin-Chênevert A, Blanchet K, Montagnon E, Belilovsky E, Wolf G, Mullie LA, Cloutier G, Chassé M, Tang A. Simulating federated learning for steatosis detection using ultrasound images. Sci Rep 2024; 14:13253. [PMID: 38858500 PMCID: PMC11164945 DOI: 10.1038/s41598-024-63969-x] [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: 02/24/2024] [Accepted: 06/04/2024] [Indexed: 06/12/2024] Open
Abstract
We aimed to implement four data partitioning strategies evaluated with four federated learning (FL) algorithms and investigate the impact of data distribution on FL model performance in detecting steatosis using B-mode US images. A private dataset (153 patients; 1530 images) and a public dataset (55 patient; 550 images) were included in this retrospective study. The datasets contained patients with metabolic dysfunction-associated fatty liver disease (MAFLD) with biopsy-proven steatosis grades and control individuals without steatosis. We employed four data partitioning strategies to simulate FL scenarios and we assessed four FL algorithms. We investigated the impact of class imbalance and the mismatch between the global and local data distributions on the learning outcome. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. AUCs were 0.93 (95% CI 0.92, 0.94) for source-based partitioning scenario with FedAvg, 0.90 (95% CI 0.89, 0.91) for a centralized model, and 0.83 (95% CI 0.81, 0.85) for a model trained in a single-center scenario. When data was perfectly balanced on the global level and each site had an identical data distribution, the model yielded an AUC of 0.90 (95% CI 0.88, 0.92). When each site contained data exclusively from one single class, irrespective of the global data distribution, the AUC fell in the range of 0.34-0.70. FL applied to B-mode US images provide performance comparable to a centralized model and higher than single-center scenario. Global data imbalance and local data heterogeneity influenced the learning outcome.
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Affiliation(s)
- Yue Qi
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | - Pedro Vianna
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Institute of Biomedical Engineering, Université de Montréal, Montréal, QC, Canada
- Laboratory of Biorheology and Medical Ultrasonics - CRCHUM, Montréal, QC, Canada
| | - Alexandre Cadrin-Chênevert
- Radiology, Radiation Oncology and Nuclear Medicine Department, Université de Montréal, Montréal, QC, Canada
- CISSS de Lanaudière, Joliette, QC, Canada
| | - Katleen Blanchet
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | - Emmanuel Montagnon
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Clinical Laboratory of Image Processing - CRCHUM, Montréal, QC, Canada
| | - Eugene Belilovsky
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
- Concordia University, Montréal, QC, Canada
| | - Guy Wolf
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
| | - Louis-Antoine Mullie
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
- Department of Medicine, Division of Critical Care Medicine, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Guy Cloutier
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Institute of Biomedical Engineering, Université de Montréal, Montréal, QC, Canada
- Laboratory of Biorheology and Medical Ultrasonics - CRCHUM, Montréal, QC, Canada
- Radiology, Radiation Oncology and Nuclear Medicine Department, Université de Montréal, Montréal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Michaël Chassé
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Department of Medicine, Division of Critical Care Medicine, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - An Tang
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada.
- Clinical Laboratory of Image Processing - CRCHUM, Montréal, QC, Canada.
- Faculty of Medicine, Université de Montréal, Montréal, QC, Canada.
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), 1058 Rue Saint-Denis, Montréal, QC, H2X 3J4, Canada.
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3
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Zamanian H, Shalbaf A, Zali MR, Khalaj AR, Dehghan P, Tabesh M, Hatami B, Alizadehsani R, Tan RS, Acharya UR. Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107932. [PMID: 38008040 DOI: 10.1016/j.cmpb.2023.107932] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/28/2023]
Abstract
BACKGROUND AND OBJECTIVES Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is important to distinguish the pathological stages of NAFLD: steatosis, steatohepatitis, and liver fibrosis, which are definitively diagnosed on invasive biopsy. Non-invasive ultrasound (US) imaging, including US elastography technique, and clinical parameters can be used to diagnose and grade NAFLD and its complications. Artificial intelligence (AI) is increasingly being harnessed for developing NAFLD diagnostic models based on clinical, biomarker, or imaging data. In this work, we systemically reviewed the literature for AI-enabled NAFLD diagnostic models based on US (including elastography) and clinical (including serological) data. METHODS We performed a comprehensive search on Google Scholar, Scopus, and PubMed search engines for articles published between January 2005 and June 2023 related to AI models for NAFLD diagnosis based on US and/or clinical parameters using the following search terms: "non-alcoholic fatty liver disease", "non-alcoholic steatohepatitis", "deep learning", "machine learning", "artificial intelligence", "ultrasound imaging", "sonography", "clinical information". RESULTS We reviewed 64 published models that used either US (including elastography) or clinical data input to detect the presence of NAFLD, non-alcoholic steatohepatitis, and/or fibrosis, and in some cases, the severity of steatosis, inflammation, and/or fibrosis as well. The performances of the published models were summarized, and stratified by data input and algorithms used, which could be broadly divided into machine and deep learning approaches. CONCLUSION AI models based on US imaging and clinical data can reliably detect NAFLD and its complications, thereby reducing diagnostic costs and the need for invasive liver biopsy. The models offer advantages of efficiency, accuracy, and accessibility, and serve as virtual assistants for specialists to accelerate disease diagnosis and reduce treatment costs for patients and healthcare systems.
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Affiliation(s)
- H Zamanian
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - A Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - M R Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - A R Khalaj
- Tehran obesity treatment center, Department of Surgery, Faculty of Medicine, Shahed University, Tehran, Iran
| | - P Dehghan
- Department of Radiology, Imaging Department, Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - M Tabesh
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research, Tehran University of Medical Sciences, Tehran, Iran
| | - B Hatami
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - R Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC, Australia
| | - Ru-San Tan
- National Heart Centre Singapore, Singapore 169609, Singapore; Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia; Centre for Health Research, University of Southern Queensland, Australia
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Yao Y, Zhang Z, Peng B, Tang J. Bio-Inspired Network for Diagnosing Liver Steatosis in Ultrasound Images. Bioengineering (Basel) 2023; 10:768. [PMID: 37508795 PMCID: PMC10376777 DOI: 10.3390/bioengineering10070768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/15/2023] [Accepted: 06/23/2023] [Indexed: 07/30/2023] Open
Abstract
Using ultrasound imaging to diagnose liver steatosis is of great significance for preventing diseases such as cirrhosis and liver cancer. Accurate diagnosis under conditions of low quality, noise and poor resolutions is still a challenging task. Physiological studies have shown that the visual cortex of the biological visual system has selective attention neural mechanisms and feedback regulation of high features to low features. When processing visual information, these cortical regions selectively focus on more sensitive information and ignore unimportant details, which can effectively extract important features from visual information. Inspired by this, we propose a new diagnostic network for hepatic steatosis. In order to simulate the selection mechanism and feedback regulation of the visual cortex in the ventral pathway, it consists of a receptive field feature extraction module, parallel attention module and feedback connection. The receptive field feature extraction module corresponds to the inhibition of the non-classical receptive field of V1 neurons on the classical receptive field. It processes the input image to suppress the unimportant background texture. Two types of attention are adopted in the parallel attention module to process the same visual information and extract different important features for fusion, which improves the overall performance of the model. In addition, we construct a new dataset of fatty liver ultrasound images and validate the proposed model on this dataset. The experimental results show that the network has good performance in terms of sensitivity, specificity and accuracy for the diagnosis of fatty liver disease.
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Affiliation(s)
- Yuan Yao
- General Practice Medical Center, West China Hospital, Sichuan University, Chengdu 610044, China
| | - Zhenguang Zhang
- School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China
| | - Bo Peng
- School of Computing and Artificial Intelligent, Southwest Jiaotong University, Chengdu 611756, China
| | - Jin Tang
- Tiaodenghe Community Health Service Center, Chengdu 610066, China
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5
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Alshagathrh FM, Househ MS. Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120748. [PMID: 36550954 PMCID: PMC9774180 DOI: 10.3390/bioengineering9120748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/20/2022] [Accepted: 10/30/2022] [Indexed: 12/05/2022]
Abstract
BACKGROUND Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. OBJECTIVE This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. METHODOLOGY A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. RESULTS Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). CONCLUSION AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary.
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6
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Kalejahi BK, Meshgini S, Danishvar S, Khorram S. Diagnosis of liver disease by computer- assisted imaging techniques: A literature review. INTELL DATA ANAL 2022. [DOI: 10.3233/ida-216379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Diagnosis of liver disease using computer-aided detection (CAD) systems is one of the most efficient and cost-effective methods of medical image diagnosis. Accurate disease detection by using ultrasound images or other medical imaging modalities depends on the physician’s or doctor’s experience and skill. CAD systems have a critical role in helping experts make accurate and right-sized assessments. There are different types of CAD systems for diagnosing different diseases, and one of the applications is in liver disease diagnosis and detection by using intelligent algorithms to detect any abnormalities. Machine learning and deep learning algorithms and models play also a big role in this area. In this article, we tried to review the techniques which are utilized in different stages of CAD systems and pursue the methods used in preprocessing, extracting, and selecting features and classification. Also, different techniques are used to segment and analyze the liver ultrasound medical images, which is still a challenging approach to how to use these techniques and their technical and clinical effectiveness as a global approach.
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Affiliation(s)
- Behnam Kiani Kalejahi
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Saeed Meshgini
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Sebelan Danishvar
- Department of Electronics and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, UK
| | - Sara Khorram
- Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
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7
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Banihashem SY, Shishehchi S. Ontology-Based decision tree model for prediction of fatty liver diseases. Comput Methods Biomech Biomed Engin 2022; 26:639-649. [PMID: 35635206 DOI: 10.1080/10255842.2022.2081502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Non-Alcohol Fatty liver disease is a common clinical complication. The paper aimed to develop a knowledge-based fatty liver detection system based on an ontology and detection rules extracted from a decision tree algorithm. Ontology is created to represent knowledge related to patients and fatty liver disease. By utilizing 43 SWRL rules and the Drool inference engine in ontology, we detected fatty liver patients. The training dataset size is 70% of clean data, including 580 electronic medical records of patients who suffer from liver diseases. After inferencing the rules, the number of patients who suffer from fatty liver disease in ontology is the same as the decision tree model. The paper validated the result generated by the ontology model through the results of the decision tree model.
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Affiliation(s)
- Seyed Yashar Banihashem
- Department Electrical and Computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin, Iran
| | - Saman Shishehchi
- Department Electrical and Computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin, Iran
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8
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Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12010521] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Fatty liver disease is considered a critical illness that should be diagnosed and detected at an early stage. In advanced stages, liver cancer or cirrhosis arise, and to identify this disease, radiologists commonly use ultrasound images. However, because of their low quality, radiologists found it challenging to recognize this disease using ultrasonic images. To avoid this problem, a Computer-Aided Diagnosis technique is developed in the current study, using Machine Learning Algorithms and a voting-based classifier to categorize liver tissues as being fatty or normal, based on extracting ultrasound image features and a voting-based classifier. Four main contributions are provided by our developed method: firstly, the classification of liver images is achieved as normal or fatty without a segmentation phase. Secondly, compared to our proposed work, the dataset in previous works was insufficient. A combination of 26 features is the third contribution. Based on the proposed methods, the extracted features are Gray-Level Co-Occurrence Matrix (GLCM) and First-Order Statistics (FOS). The fourth contribution is the voting classifier used to determine the liver tissue type. Several trials have been performed by examining the voting-based classifier and J48 algorithm on a dataset. The obtained TP, TN, FP, and FN were 94.28%, 97.14%, 5.71%, and 2.85%, respectively. The achieved precision, sensitivity, specificity, and F1-score were 94.28%, 97.05%, 94.44%, and 95.64%, respectively. The achieved classification accuracy using a voting-based classifier was 95.71% and in the case of using the J48 algorithm was 93.12%. The proposed work achieved a high performance compared with the research works.
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Baek J, Poul SS, Basavarajappa L, Reddy S, Tai H, Hoyt K, Parker KJ. Clusters of Ultrasound Scattering Parameters for the Classification of Steatotic and Normal Livers. ULTRASOUND IN MEDICINE & BIOLOGY 2021; 47:3014-3027. [PMID: 34315619 PMCID: PMC8445071 DOI: 10.1016/j.ultrasmedbio.2021.06.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 06/03/2021] [Accepted: 06/17/2021] [Indexed: 05/08/2023]
Abstract
The study of ultrasound tissue interactions in fatty livers has a long history with strong clinical potential for assessing steatosis. Recently we proposed alternative measures of first- and second-order statistics of echoes from soft tissues, namely, the H-scan, which is based on a matched filter approach, to quantify scattering transfer functions and the Burr distribution to model speckle patterns. Taken together, these approaches produce a multiparameter set that is directly related to the fundamentals of ultrasound propagation in tissue. To apply this approach to the problem of assessing steatotic livers, these analyses were applied to in vivo rat livers (N=21) under normal feeding conditions or after receiving a methionine- and choline-deficient diet that produces steatosis within a few weeks. Ultrasound data were acquired at baseline and again at weeks 2 and 6 before applying the H-scan and Burr analyses. Furthermore, a classification technique known as the support vector machine was then used to find clusters of the five parameters that are characteristic of the different steatotic liver conditions as confirmed by histologic processing of excised liver tissue samples. With the in vivo multiparametric ultrasound measurement approach and determination of clusters, steatotic can be discriminated from normal livers with 100% accuracy in a rat animal model.
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Affiliation(s)
- Jihye Baek
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA
| | - Sedigheh S Poul
- Department of Mechanical Engineering, University of Rochester, Rochester, New York, USA
| | - Lokesh Basavarajappa
- Department of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA
| | - Shreya Reddy
- Department of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA
| | - Haowei Tai
- Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, Texas, USA
| | - Kenneth Hoyt
- Department of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Kevin J Parker
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA.
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10
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Rhyou SY, Yoo JC. Cascaded Deep Learning Neural Network for Automated Liver Steatosis Diagnosis Using Ultrasound Images. SENSORS 2021; 21:s21165304. [PMID: 34450746 PMCID: PMC8398227 DOI: 10.3390/s21165304] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/27/2021] [Accepted: 08/03/2021] [Indexed: 12/13/2022]
Abstract
Diagnosing liver steatosis is an essential precaution for detecting hepatocirrhosis and liver cancer in the early stages. However, automatic diagnosis of liver steatosis from ultrasound (US) images remains challenging due to poor visual quality from various origins, such as speckle noise and blurring. In this paper, we propose a fully automated liver steatosis prediction model using three deep learning neural networks. As a result, liver steatosis can be automatically detected with high accuracy and precision. First, transfer learning is used for semantically segmenting the liver and kidney (L-K) on parasagittal US images, and then cropping the L-K area from the original US images. The second neural network also involves semantic segmentation by checking the presence of a ring that is typically located around the kidney and cropping of the L-K area from the original US images. These cropped L-K areas are inputted to the final neural network, SteatosisNet, in order to grade the severity of fatty liver disease. The experimental results demonstrate that the proposed model can predict fatty liver disease with the sensitivity of 99.78%, specificity of 100%, PPV of 100%, NPV of 99.83%, and diagnostic accuracy of 99.91%, which is comparable to the common results annotated by medical experts.
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Affiliation(s)
| | - Jae-Chern Yoo
- Correspondence: ; Tel.: +82-31-299-4591; Fax: +82-31-290-7948
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11
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Liver disease classification from ultrasound using multi-scale CNN. Int J Comput Assist Radiol Surg 2021; 16:1537-1548. [PMID: 34097226 DOI: 10.1007/s11548-021-02414-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/20/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE Ultrasound (US) is the preferred modality for fatty liver disease diagnosis due to its noninvasive, real-time, and cost-effective imaging capabilities. However, traditional B-mode US is qualitative, and therefore, the assessment is very subjective. Computer-aided diagnostic tools can improve the specificity and sensitivity of US and help clinicians to perform uniform diagnoses. METHODS In this work, we propose a novel deep learning model for nonalcoholic fatty liver disease classification from US data. We design a multi-feature guided multi-scale residual convolutional neural network (CNN) architecture to capture features of different receptive fields. B-mode US images are combined with their corresponding local phase filtered images and radial symmetry transformed images as multi-feature inputs for the network. Various fusion strategies are studied to improve prediction accuracy. We evaluate the designed network architectures on B-mode in vivo liver US images collected from 55 subjects. We also provide quantitative results by comparing our proposed multi-feature CNN architecture against traditional CNN designs and machine learning methods. RESULTS Quantitative results show an average classification accuracy above 90% over tenfold cross-validation. Our proposed method achieves a 97.8% area under the ROC curve (AUC) for the patient-specific leave-one-out cross-validation (LOOCV) evaluation. Comprehensive validation results further demonstrate that our proposed approaches achieve significant improvements compared to training mono-feature CNN architectures ([Formula: see text]). CONCLUSIONS Feature combination is valuable for the traditional classification methods, and the use of multi-scale CNN can improve liver classification accuracy. Based on the promising performance, the proposed method has the potential in practical applications to help radiologists diagnose nonalcoholic fatty liver disease.
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12
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Saffari SZ, Tabatabaey-Mashadi N, Sadeghi Bajestani G, Razmpour F, Alamdaran SA. Challenging the published fatty liver disease integrated index based on ultrasound images. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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13
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Chen CI, Chen TB, Lu NH, Du WC, Liang CY, Liu KI, Hsu SY, Lin LW, Huang YH. Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study. Proc Inst Mech Eng H 2019; 233:1100-1112. [PMID: 31441386 DOI: 10.1177/0954411919871123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The B-mode ultrasound usually contains scattering speckle noise which reduces the detailed resolution of the target and is regarded as an intrinsic noise that interferes with diagnostic precision. The aim of this study was to classify hepatic steatosis through applying attenuation correction with a phantom to reduce speckle noise in liver ultrasound tomography in patients. This retrospective study applied three randomized groups signifying different liver statuses. A total of 114 patients' effective liver ultrasound images-30 normal, 44 fatty, and 40 cancerous-were included. The proposed depth attenuation correction method was first applied to images. Three regions of interest were manually drawn on the images. Next, five feature values for the regions of interest were calculated. Finally, the hybrid method of logistic regression and support vector machine was employed to classify the ultrasound images with 10-fold cross-validation. The accuracy, kappa statistic, and mean absolute error of the proposed hybrid method were 87.5%, 0.812, and 0.119, respectively, which were higher than those of the logistic regression method-75.0%, 0.548, and 0.280-or those of the support vector machine method-75.7%, 0.637, and 0.293-respectively. Therefore, the hybrid method has been proven to be more accurate and have better performance and less error than either single method. The hybrid method provided acceptable accuracy of classification in three liver ultrasound image groups after depth attenuation correction. In the future, the deep learning approaches may be considered for the application in classifying liver ultrasound images.
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Affiliation(s)
- Chih-I Chen
- Department of Information Engineering, I-Shou University, Kaohsiung.,Division of Colon & Rectal Surgery, Department of Surgery, E-Da Hospital, I-Shou University, Kaohsiung
| | - Tai-Been Chen
- Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung
| | - Nan-Han Lu
- Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung.,Department of Radiology, E-Da Hospital, I-Shou University, Kaohsiung
| | - Wei-Chang Du
- Department of Information Engineering, I-Shou University, Kaohsiung
| | - Chih-Yu Liang
- Department of Information Engineering, I-Shou University, Kaohsiung.,Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung.,Department of Emergency Medicine, E-Da Hospital, I-Shou University, Kaohsiung
| | - Ko-Ing Liu
- Department of Radiology, E-Da Cancer Hospital, I-Shou University, Kaohsiung
| | - Shih-Yen Hsu
- Department of Information Engineering, I-Shou University, Kaohsiung
| | - Li Wei Lin
- The School of Chinese Medicine for Post-Baccalaureate, I-Shou University, Kaohsiung
| | - Yung-Hui Huang
- Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung
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Byra M, Wan L, Wong JH, Du J, Shah SB, Andre MP, Chang EY. Quantitative Ultrasound and B-Mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles. ULTRASOUND IN MEDICINE & BIOLOGY 2019; 45:1830-1840. [PMID: 30987909 DOI: 10.1016/j.ultrasmedbio.2019.02.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 02/16/2019] [Accepted: 02/25/2019] [Indexed: 06/09/2023]
Abstract
We investigate the usefulness of quantitative ultrasound and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30-MHz probe. Next, the nerves were extracted to prepare histology sections. Eighty-five fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin and ultrasound data to calculate the backscatter coefficient (-24.89 ± 8.31 dB), attenuation coefficient (0.92 ± 0.04 db/cm-MHz), Nakagami parameter (1.01 ± 0.18) and entropy (6.92 ± 0.83), as well as B-mode texture features obtained via the gray-level co-occurrence matrix algorithm. Significant Spearman rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R = -0.68), entropy (R = -0.51) and several texture features. Our study indicates that quantitative ultrasound may potentially provide information on structural components of nerve fascicles.
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Affiliation(s)
- Michal Byra
- Research Service, VA San Diego Healthcare System, San Diego, California, USA; Department of Radiology, University of California, San Diego, California, USA; Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.
| | - Lidi Wan
- Research Service, VA San Diego Healthcare System, San Diego, California, USA; Department of Radiology, University of California, San Diego, California, USA
| | - Jonathan H Wong
- Research Service, VA San Diego Healthcare System, San Diego, California, USA; Department of Radiology, University of California, San Diego, California, USA
| | - Jiang Du
- Research Service, VA San Diego Healthcare System, San Diego, California, USA; Department of Radiology, University of California, San Diego, California, USA
| | - Sameer B Shah
- Research Service, VA San Diego Healthcare System, San Diego, California, USA; Departments of Orthopedic Surgery and Bioengineering, University of California, San Diego, California, USA
| | - Michael P Andre
- Research Service, VA San Diego Healthcare System, San Diego, California, USA; Department of Radiology, University of California, San Diego, California, USA
| | - Eric Y Chang
- Research Service, VA San Diego Healthcare System, San Diego, California, USA; Department of Radiology, University of California, San Diego, California, USA
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Kriti, Virmani J, Agarwal R. Effect of despeckle filtering on classification of breast tumors using ultrasound images. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.02.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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16
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Wu CC, Yeh WC, Hsu WD, Islam MM, Nguyen PAA, Poly TN, Wang YC, Yang HC, Jack Li YC. Prediction of fatty liver disease using machine learning algorithms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 170:23-29. [PMID: 30712601 DOI: 10.1016/j.cmpb.2018.12.032] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 12/21/2018] [Accepted: 12/28/2018] [Indexed: 05/28/2023]
Abstract
BACKGROUND AND OBJECTIVE Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to develop a machine learning model to predict FLD that could assist physicians in classifying high-risk patients and make a novel diagnosis, prevent and manage FLD. METHODS We included all patients who had an initial fatty liver screening at the New Taipei City Hospital between 1st and 31st December 2009. Classification models such as random forest (RF), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) were developed to predict FLD. The area under the receiver operating characteristic curve (ROC) was used to evaluate performances among the four models. RESULTS A total of 577 patients were included in this study; of those 377 patients had fatty liver. The area under the receiver operating characteristic (AUROC) of RF, NB, ANN, and LR with 10 fold-cross validation was 0.925, 0.888, 0.895, and 0.854 respectively. Additionally, The accuracy of RF, NB, ANN, and LR 87.48, 82.65, 81.85, and 76.96%. CONCLUSION In this study, we developed and compared the four classification models to predict fatty liver disease accurately. However, the random forest model showed higher performance than other classification models. Implementation of a random forest model in the clinical setting could help physicians to stratify fatty liver patients for primary prevention, surveillance, early treatment, and management.
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Affiliation(s)
- Chieh-Chen Wu
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan
| | - Wen-Chun Yeh
- Division of Hepatogastroenterology, Department of Internal Medicine, New Taipei City Hospital, Taiwan
| | - Wen-Ding Hsu
- Division of Nephrology, Department of Internal Medicine, New Taipei City Hospital, Taiwan
| | - Md Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan
| | - Phung Anh Alex Nguyen
- International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan
| | - Yao-Chin Wang
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan; Department of Emergency, Min-Sheng General Hospital, Taoyuan, Taiwan
| | - Hsuan-Chia Yang
- International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan
| | - Yu-Chuan Jack Li
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology(ICHIT), Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan.
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Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Assist Radiol Surg 2018; 13:1895-1903. [PMID: 30094778 PMCID: PMC6223753 DOI: 10.1007/s11548-018-1843-2] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Accepted: 07/31/2018] [Indexed: 02/06/2023]
Abstract
PURPOSE The nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound. METHODS We used the Inception-ResNet-v2 deep convolutional neural network pre-trained on the ImageNet dataset to extract high-level features in liver B-mode ultrasound image sequences. The steatosis level of each liver was graded by wedge biopsy. The proposed approach was compared with the hepatorenal index technique and the gray-level co-occurrence matrix algorithm. After the feature extraction, we applied the support vector machine algorithm to classify images containing fatty liver. Based on liver biopsy, the fatty liver was defined to have more than 5% of hepatocytes with steatosis. Next, we used the features and the Lasso regression method to assess the steatosis level. RESULTS The area under the receiver operating characteristics curve obtained using the proposed approach was equal to 0.977, being higher than the one obtained with the hepatorenal index method, 0.959, and much higher than in the case of the gray-level co-occurrence matrix algorithm, 0.893. For regression the Spearman correlation coefficients between the steatosis level and the proposed approach, the hepatorenal index and the gray-level co-occurrence matrix algorithm were equal to 0.78, 0.80 and 0.39, respectively. CONCLUSIONS The proposed approach may help the sonographers automatically diagnose the amount of fat in the liver. The presented approach is efficient and in comparison with other methods does not require the sonographers to select the region of interest.
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18
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Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2017.12.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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HAGIWARA YUKI, SUDARSHAN VIDYAK, LEONG SOOKSAM, VIJAYNANTHAN ANUSHYA, NG KWANHOONG. APPLICATION OF ENTROPIES FOR AUTOMATED DIAGNOSIS OF ABNORMALITIES IN ULTRASOUND IMAGES: A REVIEW. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400127] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Automation of diagnosis process in medical imaging using various computer-aided techniques is a leading topic of research. Among many computer-aided methods, nonlinear entropies are widely applied in the development of automated algorithms to diagnose abnormalities present in medical images. The use of entropy features in development of Computer-Aided Diagnosis (CAD) may enhance the accuracy of the system. Entropy features depict the nonlinearity of images and thereby the presence of complexity in the images. Various types of entropies have been employed in medical image analysis for automated diagnosis of abnormalities present in the images. This paper focuses on the diverse types of entropies employed in the development of CAD systems for the diagnosis of abnormalities in the medical images. In addition to the diagnosis, these entropies can be used to differentiate the images based on the severity of the abnormalities and for other biomedical applications.
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Affiliation(s)
- YUKI HAGIWARA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - VIDYA K SUDARSHAN
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Science, Singapore
- School of Electrical and Computer Engineering, University of Newcastle, Singapore
| | - SOOK SAM LEONG
- Department of Biomedical Imaging, University of Malaya, Malaysia
- University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Malaysia
| | - ANUSHYA VIJAYNANTHAN
- Department of Biomedical Imaging, University of Malaya, Malaysia
- University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Malaysia
| | - KWAN HOONG NG
- Department of Biomedical Imaging, University of Malaya, Malaysia
- University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Malaysia
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Owjimehr M, Danyali H, Helfroush MS, Shakibafard A. Staging of Fatty Liver Diseases Based on Hierarchical Classification and Feature Fusion for Back-Scan-Converted Ultrasound Images. ULTRASONIC IMAGING 2017; 39:79-95. [PMID: 27694278 DOI: 10.1177/0161734616649153] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Fatty liver disease is progressive and may not cause any symptoms at early stages. This disease is potentially fatal and can cause liver cancer in severe stages. Therefore, diagnosing and staging fatty liver disease in early stages is necessary. In this paper, a novel method is presented to classify normal and fatty liver, as well as discriminate three stages of fatty liver in ultrasound images. This study is performed with 129 subjects including 28 normal, 47 steatosis, 42 fibrosis, and 12 cirrhosis images. The proposed approach uses back-scan conversion of ultrasound sector images and is based on a hierarchical classification. The proposed algorithm is performed in two parts. The first part selects the optimum regions of interest from the focal zone of the back-scan-converted ultrasound images. In the second part, discrimination between normal and fatty liver is performed and then steatosis, fibrosis, and cirrhosis are classified in a hierarchical basis. The wavelet packet transform and gray-level co-occurrence matrix are used to obtain a number of statistical features. A support vector machine classifier is used to discriminate between normal and fatty liver, and stage fatty cases. The results of the proposed scheme clearly illustrate the efficiency of this system with overall accuracy of 94.91% and also specificity of more than 90%.
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Bharti P, Mittal D, Ananthasivan R. Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review. ULTRASONIC IMAGING 2017; 39:33-61. [PMID: 27097589 DOI: 10.1177/0161734616639875] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis.
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Affiliation(s)
- Puja Bharti
- 1 Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, India
| | - Deepti Mittal
- 1 Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, India
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Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images. Comput Biol Med 2016; 79:250-258. [PMID: 27825038 DOI: 10.1016/j.compbiomed.2016.10.022] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 10/26/2016] [Accepted: 10/27/2016] [Indexed: 02/07/2023]
Abstract
Fatty liver disease (FLD) is reversible disease and can be treated, if it is identified at an early stage. However, if diagnosed at the later stage, it can progress to an advanced liver disease such as cirrhosis which may ultimately lead to death. Therefore, it is essential to detect it at an early stage before the disease progresses to an irreversible stage. Several non-invasive computer-aided techniques are proposed to assist in the early detection of FLD and cirrhosis using ultrasound images. In this work, we are proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method. Higher order spectra (HOS) bispectrum, HOS phase, fuzzy, Kapoor, max, Renyi, Shannon, Vajda and Yager entropies are extracted from CT coefficients. These extracted features are subjected to locality sensitive discriminant analysis (LSDA) feature reduction method. Then these LSDA coefficients ranked based on F-value are fed to different classifiers to choose the best performing classifier using minimum number of features. Our proposed technique can characterize normal, FLD and cirrhosis using probabilistic neural network (PNN) classifier with an accuracy of 97.33%, specificity of 100.00% and sensitivity of 96.00% using only six features. In addition, these chosen features are used to develop a liver disease index (LDI) to differentiate the normal, FLD and cirrhosis classes using a single number. This can significantly help the radiologists to discriminate FLD and cirrhosis in their routine liver screening.
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23
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Goceri E, Shah ZK, Layman R, Jiang X, Gurcan MN. Quantification of liver fat: A comprehensive review. Comput Biol Med 2016; 71:174-89. [PMID: 26945465 DOI: 10.1016/j.compbiomed.2016.02.013] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Revised: 02/18/2016] [Accepted: 02/19/2016] [Indexed: 12/19/2022]
Abstract
Fat accumulation in the liver causes metabolic diseases such as obesity, hypertension, diabetes or dyslipidemia by affecting insulin resistance, and increasing the risk of cardiac complications and cardiovascular disease mortality. Fatty liver diseases are often reversible in their early stage; therefore, there is a recognized need to detect their presence and to assess its severity to recognize fat-related functional abnormalities in the liver. This is crucial in evaluating living liver donors prior to transplantation because fat content in the liver can change liver regeneration in the recipient and donor. There are several methods to diagnose fatty liver, measure the amount of fat, and to classify and stage liver diseases (e.g. hepatic steatosis, steatohepatitis, fibrosis and cirrhosis): biopsy (the gold-standard procedure), clinical (medical physics based) and image analysis (semi or fully automated approaches). Liver biopsy has many drawbacks: it is invasive, inappropriate for monitoring (i.e., repeated evaluation), and assessment of steatosis is somewhat subjective. Qualitative biomarkers are mostly insufficient for accurate detection since fat has to be quantified by a varying threshold to measure disease severity. Therefore, a quantitative biomarker is required for detection of steatosis, accurate measurement of severity of diseases, clinical decision-making, prognosis and longitudinal monitoring of therapy. This study presents a comprehensive review of both clinical and automated image analysis based approaches to quantify liver fat and evaluate fatty liver diseases from different medical imaging modalities.
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Affiliation(s)
- Evgin Goceri
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, USA.
| | - Zarine K Shah
- Department of Radiology, Wexner Medical Center, The Ohio State University, Columbus, USA
| | - Rick Layman
- Department of Radiology, Wexner Medical Center, The Ohio State University, Columbus, USA
| | - Xia Jiang
- Department of Radiology, Wexner Medical Center, The Ohio State University, Columbus, USA
| | - Metin N Gurcan
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, USA
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Alivar A, Danyali H, Helfroush MS. Hierarchical classification of normal, fatty and heterogeneous liver diseases from ultrasound images using serial and parallel feature fusion. Biocybern Biomed Eng 2016. [DOI: 10.1016/j.bbe.2016.07.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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