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Yi PH, Garner HW, Hirschmann A, Jacobson JA, Omoumi P, Oh K, Zech JR, Lee YH. Clinical Applications, Challenges, and Recommendations for Artificial Intelligence in Musculoskeletal and Soft-Tissue Ultrasound: AJR Expert Panel Narrative Review. AJR Am J Roentgenol 2024; 222:e2329530. [PMID: 37436032 DOI: 10.2214/ajr.23.29530] [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: 07/13/2023]
Abstract
Artificial intelligence (AI) is increasingly used in clinical practice for musculoskeletal imaging tasks, such as disease diagnosis and image reconstruction. AI applications in musculoskeletal imaging have focused primarily on radiography, CT, and MRI. Although musculoskeletal ultrasound stands to benefit from AI in similar ways, such applications have been relatively underdeveloped. In comparison with other modalities, ultrasound has unique advantages and disadvantages that must be considered in AI algorithm development and clinical translation. Challenges in developing AI for musculoskeletal ultrasound involve both clinical aspects of image acquisition and practical limitations in image processing and annotation. Solutions from other radiology subspecialties (e.g., crowdsourced annotations coordinated by professional societies), along with use cases (most commonly rotator cuff tendon tears and palpable soft-tissue masses), can be applied to musculoskeletal ultrasound to help develop AI. To facilitate creation of high-quality imaging datasets for AI model development, technologists and radiologists should focus on increasing uniformity in musculoskeletal ultrasound performance and increasing annotations of images for specific anatomic regions. This Expert Panel Narrative Review summarizes available evidence regarding AI's potential utility in musculoskeletal ultrasound and challenges facing its development. Recommendations for future AI advancement and clinical translation in musculoskeletal ultrasound are discussed.
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Affiliation(s)
- Paul H Yi
- University of Maryland Medical Intelligent Imaging Center, University of Maryland School of Medicine, Baltimore, MD
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD
| | | | - Anna Hirschmann
- Imamed Radiology Nordwest, Basel, Switzerland
- Department of Radiology, University of Basel, Basel, Switzerland
| | - Jon A Jacobson
- Lenox Hill Radiology, New York, NY
- Department of Radiology, University of California, San Diego Medical Center, San Diego, CA
| | - Patrick Omoumi
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
- Department of Radiology, University of Lausanne, Lausanne, Switzerland
| | - Kangrok Oh
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea
| | - John R Zech
- Department of Radiology, Columbia University Irving Medical Center, New York-Presbyterian Hospital, New York, NY
| | - Young Han Lee
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea
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Mahendrakar P, Kumar D, Patil U. A Comprehensive Review on MRI-based Knee Joint Segmentation and Analysis Techniques. Curr Med Imaging 2024; 20:e150523216894. [PMID: 37189281 DOI: 10.2174/1573405620666230515090557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/29/2022] [Accepted: 12/28/2022] [Indexed: 05/17/2023]
Abstract
Using magnetic resonance imaging (MRI) in osteoarthritis pathogenesis research has proven extremely beneficial. However, it is always challenging for both clinicians and researchers to detect morphological changes in knee joints from magnetic resonance (MR) imaging since the surrounding tissues produce identical signals in MR studies, making it difficult to distinguish between them. Segmenting the knee bone, articular cartilage and menisci from the MR images allows one to examine the complete volume of the bone, articular cartilage, and menisci. It can also be used to assess certain characteristics quantitatively. However, segmentation is a laborious and time-consuming operation that requires sufficient training to complete correctly. With the advancement of MRI technology and computational methods, researchers have developed several algorithms to automate the task of individual knee bone, articular cartilage and meniscus segmentation during the last two decades. This systematic review aims to present available fully and semi-automatic segmentation methods for knee bone, cartilage, and meniscus published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field of image analysis and segmentation, which helps the development of novel automated methods for clinical applications. The review also contains the recently developed fully automated deep learning-based methods for segmentation, which not only provides better results compared to the conventional techniques but also open a new field of research in Medical Imaging.
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Affiliation(s)
- Pavan Mahendrakar
- BLDEA’s V.P.Dr. P.G., Halakatti College of Engineering and Technology, Vijayapur, Karnataka, India
| | | | - Uttam Patil
- Jain College of Engineering, T.S Nagar, Hunchanhatti Road, Machhe, Belagavi, Karnataka, India
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Yeoh PSQ, Lai KW, Goh SL, Hasikin K, Wu X, Li P. Transfer learning-assisted 3D deep learning models for knee osteoarthritis detection: Data from the osteoarthritis initiative. Front Bioeng Biotechnol 2023; 11:1164655. [PMID: 37122858 PMCID: PMC10136763 DOI: 10.3389/fbioe.2023.1164655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 03/24/2023] [Indexed: 05/02/2023] Open
Abstract
Knee osteoarthritis is one of the most common musculoskeletal diseases and is usually diagnosed with medical imaging techniques. Conventionally, case identification using plain radiography is practiced. However, we acknowledge that knee osteoarthritis is a 3D complexity; hence, magnetic resonance imaging will be the ideal modality to reveal the hidden osteoarthritis features from a three-dimensional view. In this work, the feasibility of well-known convolutional neural network (CNN) structures (ResNet, DenseNet, VGG, and AlexNet) to distinguish knees with and without osteoarthritis (OA) is investigated. Using 3D convolutional layers, we demonstrated the potential of 3D convolutional neural networks of 13 different architectures in knee osteoarthritis diagnosis. We used transfer learning by transforming 2D pre-trained weights into 3D as initial weights for the training of the 3D models. The performance of the models was compared and evaluated based on the performance metrics [balanced accuracy, precision, F1 score, and area under receiver operating characteristic (AUC) curve]. This study suggested that transfer learning indeed enhanced the performance of the models, especially for ResNet and DenseNet models. Transfer learning-based models presented promising results, with ResNet34 achieving the best overall accuracy of 0.875 and an F1 score of 0.871. The results also showed that shallow networks yielded better performance than deeper neural networks, demonstrated by ResNet18, DenseNet121, and VGG11 with AUC values of 0.945, 0.914, and 0.928, respectively. This encourages the application of clinical diagnostic aid for knee osteoarthritis using 3DCNN even in limited hardware conditions.
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Affiliation(s)
| | - Khin Wee Lai
- Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- *Correspondence: Khin Wee Lai, ; Pei Li,
| | - Siew Li Goh
- Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Xiang Wu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Pei Li
- Information Department, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
- *Correspondence: Khin Wee Lai, ; Pei Li,
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Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1847981. [PMID: 35602622 PMCID: PMC9119795 DOI: 10.1155/2022/1847981] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/07/2022] [Accepted: 04/09/2022] [Indexed: 11/24/2022]
Abstract
Plaque deposits in the carotid artery are the major cause of stroke and atherosclerosis. Ultrasound imaging is used as an early indicator of disease progression. Classification of the images to identify plaque presence and intima-media thickness (IMT) by machine learning algorithms requires features extracted from the images. A total of 361 images were used for feature extraction, which will assist in further classification of the carotid artery. This study presents the extraction of 65 features, which constitute of shape, texture, histogram, correlogram, and morphology features. Principal component analysis (PCA)-based feature selection is performed, and the 22 most significant features, which will improve the classification accuracy, are selected. Naive Bayes algorithm and dynamic learning vector quantization (DLVQ)-based machine learning classifications are performed with the extracted and selected features, and analysis is performed.
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AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency? Skeletal Radiol 2022; 51:293-304. [PMID: 34341865 DOI: 10.1007/s00256-021-03876-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 07/21/2021] [Accepted: 07/21/2021] [Indexed: 02/02/2023]
Abstract
Artificial intelligence (AI) is expected to bring greater efficiency in radiology by performing tasks that would otherwise require human intelligence, also at a much faster rate than human performance. In recent years, milestone deep learning models with unprecedented low error rates and high computational efficiency have shown remarkable performance for lesion detection, classification, and segmentation tasks. However, the growing field of AI has significant implications for radiology that are not limited to visual tasks. These are essential applications for optimizing imaging workflows and improving noninterpretive tasks. This article offers an overview of the recent literature on AI, focusing on the musculoskeletal imaging chain, including initial patient scheduling, optimized protocoling, magnetic resonance imaging reconstruction, image enhancement, medical image-to-image translation, and AI-aided image interpretation. The substantial developments of advanced algorithms, the emergence of massive quantities of medical data, and the interest of researchers and clinicians reveal the potential for the growing applications of AI to augment the day-to-day efficiency of musculoskeletal radiologists.
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Verma DK, Kumari P, Kanagaraj S. Engineering Aspects of Incidence, Prevalence, and Management of Osteoarthritis: A Review. Ann Biomed Eng 2022; 50:237-252. [DOI: 10.1007/s10439-022-02913-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 01/01/2022] [Indexed: 12/14/2022]
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A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images. Comput Biol Med 2021; 141:105117. [PMID: 34968861 DOI: 10.1016/j.compbiomed.2021.105117] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Rheumatoid arthritis (RA) is a chronic disease characterized by erosive symmetrical polyarthritis. Bone and cartilage are the main joint targets of this disease. Cartilage damage is one of the most relevant determinants of physical disability in RA patients. Cartilage damage is nowadays assessed by clinicians, which manually measure cartilage thickness in ultrasound (US) imaging. This poses issues relevant to intra-and inter-observer variability. Relying on the acquisition of metacarpal-head US images from 38 subjects, this work addresses the problem of automatic cartilage-thickness measurement by designing a new deep-learning (DL) framework. METHODS The framework consists of a Convolutional Neural Network (CNN), responsible for regressing cartilage-interface distance fields, followed by a post-processing step to delineate the two cartilage interfaces from the predicted distance fields and compute the cartilage thickness. RESULTS Our framework achieved encouraging results with a mean absolute difference (ADF) of 0.032 (±0.026) mm against manual thickness annotation by an expert clinician. When evaluating the intra-observer variability, we obtained an ADF = 0.036 (±0.028) mm. CONCLUSION The proposed framework achieved an ADF against manual annotation that was comparable to the intra-observer variability, proving the potential of DL in the field. SIGNIFICANCE This work is the first to address the problem of automatic cartilage-thickness estimation in US rheumatological images with DL, paving the way for future research in the field. From a clinical perspective, the proposed framework proved to be a valuable tool to support the clinical routine increasing the reproducibility of cartilage thickness measurements.
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Emergence of Deep Learning in Knee Osteoarthritis Diagnosis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:4931437. [PMID: 34804143 PMCID: PMC8598325 DOI: 10.1155/2021/4931437] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/23/2021] [Accepted: 10/25/2021] [Indexed: 12/13/2022]
Abstract
Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency. Medical imaging processes, especially those that produce 3-dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state-of-the-art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.
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Khamparia A, Pandey B, Al‐Turjman F, Podder P. An intelligent
IoMT
enabled feature extraction method for early detection of knee arthritis. EXPERT SYSTEMS 2021. [DOI: 10.1111/exsy.12784] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Aditya Khamparia
- Department of Computer Science Babasaheb Bhimrao Ambedkar University, Satellite Center, Amethi Amethi India
| | - Babita Pandey
- Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow India
| | - Fadi Al‐Turjman
- Artificial Intelligence Engineering Department Research Center for AI and IoT, Near East University Nicosia Turkey
| | - Prajoy Podder
- Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh
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Shin Y, Yang J, Lee YH, Kim S. Artificial intelligence in musculoskeletal ultrasound imaging. Ultrasonography 2021; 40:30-44. [PMID: 33242932 PMCID: PMC7758096 DOI: 10.14366/usg.20080] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 09/04/2020] [Accepted: 09/06/2020] [Indexed: 12/14/2022] Open
Abstract
Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging that facilitates the rapid and dynamic assessment of musculoskeletal components. Significant technological improvements have contributed to the increasing adoption of US for musculoskeletal assessments, as artificial intelligence (AI)-based computer-aided detection and computer-aided diagnosis are being utilized to improve the quality, efficiency, and cost of US imaging. This review provides an overview of classical machine learning techniques and modern deep learning approaches for musculoskeletal US, with a focus on the key categories of detection and diagnosis of musculoskeletal disorders, predictive analysis with classification and regression, and automated image segmentation. Moreover, we outline challenges and a range of opportunities for AI in musculoskeletal US practice.
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Affiliation(s)
- YiRang Shin
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Korea
| | - Jaemoon Yang
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Korea
- Systems Molecular Radiology at Yonsei (SysMolRaY), Seoul, Korea
- Severance Biomedical Science Institute (SBSI), Yonsei University College of Medicine, Seoul, Korea
| | - Young Han Lee
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Korea
| | - Sungjun Kim
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Korea
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Shoaib MA, Hossain MB, Hum YC, Chuah JH, Mohd Salim MI, Lai KW. Speckle Noise Diffusion in Knee Articular Cartilage Ultrasound Images. Curr Med Imaging 2020; 16:739-751. [PMID: 32723246 DOI: 10.2174/1573405615666190903143330] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 06/15/2019] [Accepted: 08/17/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Ultrasound (US) imaging can be a convenient and reliable substitute for magnetic resonance imaging in the investigation or screening of articular cartilage injury. However, US images suffer from two main impediments, i.e., low contrast ratio and presence of speckle noise. AIMS A variation of anisotropic diffusion is proposed that can reduce speckle noise without compromising the image quality of the edges and other important details. METHODS For this technique, four gradient thresholds were adopted instead of one. A new diffusivity function that preserves the edge of the resultant image is also proposed. To automatically terminate the iterative procedures, the Mean Absolute Error as its stopping criterion was implemented. RESULTS Numerical results obtained by simulations unanimously indicate that the proposed method outperforms conventional speckle reduction techniques. Nevertheless, this preliminary study has been conducted based on a small number of asymptomatic subjects. CONCLUSION Future work must investigate the feasibility of this method in a large cohort and its clinical validity through testing subjects with a symptomatic cartilage injury.
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Affiliation(s)
- Muhammad Ali Shoaib
- Department of Biomedical Engineering, Faculty of Engineering, University Malaya, 50603, Kuala Lumpur, Malaysia
| | - Md Belayet Hossain
- School of Information Technology, Deakin University, Melbourne, Australia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Cheras, Kajang 43000, Selangor, Darul Ehsan, Malaysia
| | - Joon Huang Chuah
- VIP Research Lab, Department of Electrical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia
| | - Maheza Irna Mohd Salim
- Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University Malaya, 50603, Kuala Lumpur, Malaysia
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Nizar MHA, Chan CK, Khalil A, Yusof AKM, Lai KW. Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks. Curr Med Imaging 2020; 16:584-591. [PMID: 32484093 DOI: 10.2174/1573405615666190114151255] [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] [Received: 10/11/2018] [Revised: 12/14/2018] [Accepted: 12/20/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. METHODS Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. RESULTS Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. CONCLUSION Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.
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Affiliation(s)
- Muhammad Hanif Ahmad Nizar
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia
| | - Chow Khuen Chan
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia
| | - Azira Khalil
- Department of Applied Physics, Islamic Science University of Malaysia, Nilai, Negeri Sembilan 71800, Malaysia
| | | | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia
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Desai P, Hacihaliloglu I. Knee-Cartilage Segmentation and Thickness Measurement from 2D Ultrasound. J Imaging 2019; 5:jimaging5040043. [PMID: 34460481 PMCID: PMC8320944 DOI: 10.3390/jimaging5040043] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 03/26/2019] [Accepted: 03/26/2019] [Indexed: 11/20/2022] Open
Abstract
Ultrasound (US) could become a standard of care imaging modality for the quantitative assessment of femoral cartilage thickness for the early diagnosis of knee osteoarthritis. However, low contrast, high levels of speckle noise, and various imaging artefacts hinder the analysis of collected data. Accurate, robust, and fully automatic US image-enhancement and cartilage-segmentation methods are needed in order to improve the widespread deployment of this imaging modality for knee-osteoarthritis diagnosis and monitoring. In this work, we propose a method based on local-phase-based image processing for automatic knee-cartilage image enhancement, segmentation, and thickness measurement. A local-phase feature-guided dynamic-programming approach is used for the fully automatic localization of knee-bone surfaces. The localized bone surfaces are used as seed points for automating the seed-guided segmentation of the cartilage. We evaluated the Random Walker (RW), watershed, and graph-cut-based segmentation methods from 200 scans obtained from ten healthy volunteers. Validation against manual expert segmentation achieved a mean dice similarity coefficient of 0.90, 0.86, and 0.84 for the RW, watershed, and graph-cut segmentation methods, respectively. Automatically segmented cartilage regions achieved 0.18 mm localization accuracy compared to manual expert thickness measurement.
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Affiliation(s)
- Prajna Desai
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA
| | - Ilker Hacihaliloglu
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA
- Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 08873, USA
- Correspondence: ; Tel.: +1-848-445-6564
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