A fuzzy C-means quantization based automatic extraction of rotator cuff tendon tears from ultrasound images

Background: Chronic non-traumatic rotator cuff tendon tears are inextricably linked with the natural process of aging often resulting in severe disability, poor quality of life and an added burden to the health care system. The occurrence of rotator cuff tendon tears increases exponentially with every decade of life to approximately 60% in individuals over 80 years of age. Exercise is a commonly prescribed intervention although research on its efficacy is in its infancy and often conflicting. The purpose of this systematic review is to investigate the effectiveness of exercise interventions for people diagnosed with large to massive rotator cuff tendon tears. Methods: This systematic review will adhere to the PRISMA reporting guidelines. A comprehensive search of five databases will be conducted. Randomised clinical trials (RCT) or quasi-randomised control trials will be included if they evaluate exercise as the core intervention or as part of the intervention in the management of large to massive rotator cuff tears. To quantify response to treatment we will compare changes in pain, disability and quality of life (QoL). The Consensus on Exercise Reporting Template (CERT) will be used to characterise the different types of exercise intervention. The Cochrane Risk of Bias Tool will be used to assess study quality. A narrative synthesis with meta-analysis will be performed, and the certainty of evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. Discussion: This review will synthesise the totality of GRADE A and B evidence on the effectiveness of exercise for large to massive rotator cuff tendon tears. It will provide clinically important information and guidance for immediate implementation by clinicians, health policymakers and may be used to guide future research. PROSPERO registration: 244502 (24/03/2021)

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Tendon Tissue Engineering: The Potential Application of Stem Cells, Biological Factors, and Repair Scaffolds to Improve Rotator Cuff Tendon Tears

Comprehensive Biotechnology ◽

10.1016/b978-0-444-64046-8.00297-4 ◽

2011 ◽

pp. 252-269

Author(s):

S. Chaudhury ◽

R.J. Murphy ◽

A.J. Carr

Keyword(s):

Tissue Engineering ◽

Stem Cells ◽

Rotator Cuff ◽

Potential Application ◽

Biological Factors ◽

Tendon Tissue ◽

Rotator Cuff Tendon ◽

Tendon Tissue Engineering ◽

Tendon Tears

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Tear characteristics and surgeon influence repair technique and suture anchor use in repair of superior-posterior rotator cuff tendon tears

Journal of Shoulder and Elbow Surgery ◽

10.1016/j.jse.2018.07.028 ◽

2019 ◽

Vol 28 (2) ◽

pp. 227-236 ◽

Cited By ~ 5

Author(s):

Kathleen A. Derwin ◽

Sambit Sahoo ◽

Alexander Zajichek ◽

Gregory Strnad ◽

Kurt P. Spindler ◽

...

Keyword(s):

Rotator Cuff ◽

Suture Anchor ◽

Rotator Cuff Tendon ◽

Repair Technique ◽

Tendon Tears

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Semi-dynamic Control of FCM Initialization for Automatic Extraction of Inflamed Appendix from Ultrasonography

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405614666180719142536 ◽

2019 ◽

Vol 15 (8) ◽

pp. 810-816

Author(s):

Kwang Baek Kim ◽

Hyun Jun Park ◽

Doo Heon Song

Keyword(s):

Learning Algorithm ◽

Dynamic Control ◽

False Negative ◽

Automatic Extraction ◽

Ultrasound Images ◽

Self Organizing Map ◽

Number Of Clusters ◽

Suspected Appendicitis ◽

Fuzzy C Means ◽

Intensity Contrast

Background: Current naked-eye examination of the ultrasound images for inflamed appendix has limitations due to its intrinsic operator subjectivity problem. Objective: In this paper, we propose a fully automatic intelligent method for extracting inflamed appendix from ultrasound images. Accurate and automatic extraction of inflamed appendix from ultrasonography is a major decision making resource of the diagnosis and management of suspected appendicitis. Methods: The proposed method uses Fuzzy C-means learning algorithm in pixel clustering with semi-dynamic control of initializing the number of clusters based on the intensity contrast dispersion of the input image. Thirty percent of the prepared ultrasonography samples are classified into four different groups based on their intensity contrast distribution and then different number of clusters are assigned to the images in accordance with such groups in Fuzzy C-means learning process. Results: In the experiment, the proposed system successfully extracts the target without human intervention in 82 of 85 cases (96.47% accuracy). The proposed method also shows that it can cover the false negative cases occurred previously that used self-organizing map as the learning engine. Conclusion: Such high level reliable correct extraction of inflamed appendix encourages to use the automatic extraction software in the diagnosis procedure of suspected acute appendicitis.

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