A Novel Method of Synovitis Stratification in Ultrasound Using Machine Learning Algorithms: Results From Clinical Validation of the MEDUSA Project

Mielnik, Paweł; Fojcik, Marcin; Segen, Jakub; Kulbacki, Marek

doi:10.1016/j.ultrasmedbio.2017.10.005

Cited by 14 publications

(15 citation statements)

References 7 publications

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“…In this article, we tested our proposed methods on images from MEDUSA database. 18 The database contains a set of 276 images with manually annotated bone, joint and synovial regions. The proposed method was compared with the annotated image for localization of each region.…”

Section: Resultsmentioning

confidence: 99%

“…The segmentation of bone and joint region has been addressed by researchers with techniques based on pixel intensity, region and linear features. [15][16][17][18][19][20] In our initial state of work, we subjected to segment different anatomical regions from the raw ultrasound images using deep learning where the determination of structural difference among the grades was not commendable. Hence, the anatomical regions were segmented using pixel intensity-based algorithms.…”

Section: Methodsmentioning

confidence: 99%

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Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning

Hemalatha

Vijaybaskar

Thamizhvani

2019

Proc Inst Mech Eng H

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The pace of population aging is growing faster worldwide. The quality of life of the aging population is mostly affected by rheumatic diseases. With the increasing rate of rheumatoid arthritis in the aging population, technological advances in the field of automatic image processing and analysis have paved way for automatic detection and diagnosis of arthritis based on how the grade of the synovial region is designed. The proposed method is based on spatial analysis using intensity-based approach to segment the skin border, thresholding and connectivity algorithm for bone region segmentation, hit-or-miss transform for bone line segmentation and distance measure with image profile to detect the joint region. After this process of localization, the synovial region is determined using the active contour technique. In arthritis condition, synovitis also occurs which is categorized into four different grades based on the fluid expansion in the synovial region. The different grades are defined and analyzed through deep learning. Convolutional neural network in a deep learning algorithm is used to diagnose the particular grade of synovitis to describe the nature of arthritis. With these results, a module to detect the nature of arthritis automatically is defined.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning

Hemalatha

Vijaybaskar

Thamizhvani

2019

Proc Inst Mech Eng H

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“…The level set segmentation methods like Caselles, Chan-Vese, Bernard, Li, and Lankton are applied on arthritis affected finger joint images obtained from the MEDUSA database http://medusa.aei.polsl.pl. [16][17][18]. Further using performance analysis metrics like dice coefficient and Hausdroff distance and statistical analysis metrics like standard error and F-test describes the significant difference between the techniques used for segmentation.…”

Section: Methodsmentioning

confidence: 99%

Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

Hemalatha

Vijaybaskar

Thamizhvani

2018

Advances in Multimedia

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Active contour methods are widely used for medical image segmentation. Using level set algorithms the applications of active contour methods have become flexible and convenient. This paper describes the evaluation of the performance of the active contour models using performance metrics and statistical analysis. We have implemented five different methods for segmenting the synovial region in arthritis affected ultrasound image. A comparative analysis between the methods of segmentation was performed and the best segmentation method was identified using similarity criteria, standard error, and F-test. For further analysis, classification of the segmentation techniques using support vector machine (SVM) classifier is performed to determine the absolute method for synovial region detection. With these results, localized region based active contour named Lankton method is defined to be the best segmentation method.

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“…2A). A comprehensive review of ML studies shows numerous highquality techniques to evaluate musculoskeletal disorders based on US images (Table 1) [10][11][12][13][14][15]. However, a major challenge facing these approaches is that feature selection heavily relies on statistical insights and domain knowledge, and this limitation initiated a paradigm shift from manual feature engineering to DL architectural design.…”

Section: Ml: Feature Extraction and Classification Algorithmsmentioning

confidence: 99%

Artificial intelligence in musculoskeletal ultrasound imaging

et al. 2021

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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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A Novel Method of Synovitis Stratification in Ultrasound Using Machine Learning Algorithms: Results From Clinical Validation of the MEDUSA Project

Cited by 14 publications

References 7 publications

Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning

Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning

Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

Artificial intelligence in musculoskeletal ultrasound imaging

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