@inproceedings{aba2409ffad642b1b32e9c02b11c27ad,
title = "Ultrasound image analysis for myopathy detection",
abstract = "This study focuses on using ultrasound (US) biomarkers for characterizing myopathies and in particular myositis. US offers an opportunity to deliver diagnostics in clinical settings at a fraction of the cost and discomfort entailed in current workflows. US is also better suited for usage in under-resourced environments. This paper is focused on studying the link between biomarkers related to absolute and relative echo intensity of muscle tissue and the presence and severity of myositis disease. We show that there is good correlation between these biomarkers and the severity of muscle disease rated by the Heckmatt criteria. A moderate correlation is also found between these biomarkers and muscles categorized by healthy vs. diseased status of each patient. Experimental data involving 37 patients (9 polymyositis, 3 dermatomyositis, 9 inclusion body myositis, and 16 healthy patients) and seven muscle groups show correlations up to 0.91.",
keywords = "Image biomarkers, Myopathy, Myositis, Regression, Ultrasound",
author = "Seth Billings and Jemima Albayda and Philippe Burlina",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd International Conference on Pattern Recognition, ICPR 2016 ; Conference date: 04-12-2016 Through 08-12-2016",
year = "2016",
month = jan,
day = "1",
doi = "10.1109/ICPR.2016.7899843",
language = "English (US)",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1461--1465",
booktitle = "2016 23rd International Conference on Pattern Recognition, ICPR 2016",
}