Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Shahid, Muhammad Laiq Ur Rahman; Chitiboi, Teodora; Ivanovska, Tetyana; Molchanov, Vladimir; Völzke, Henry; Linsen, Lars
• Is Part Of: BMC medical imaging. Volume 17:Issue 1(2017)
• Page Start: 1
• Page End: 13
• Publication Date: 2017-12
• Subjects: Para-pharyngeal fat pads segmentation -- Upper airway segmentation -- Interactive visual analysis tool -- Obstructive sleep apnea (OSA) -- Magnetic resonance imaging (MRI); Diagnostic imaging -- Periodicals; 616.075405
• DOI: 10.1186/s12880-017-0179-7
• Languages: English
• ISSNs: 1471-2342

Abstract

Abstract Background Obstructive sleep apnea (OSA) is a public health problem. Detailed analysis of the para-pharyngeal fat pads can help us to understand the pathogenesis of OSA and may mediate the intervention of this sleeping disorder. A reliable and automatic para-pharyngeal fat pads segmentation technique plays a vital role in investigating larger data bases to identify the anatomic risk factors for the OSA. Methods Our research aims to develop a context-based automatic segmentation algorithm to delineate the fat pads from magnetic resonance images in a population-based study. Our segmentation pipeline involves texture analysis, connected component analysis, object-based image analysis, and supervised classification using an interactive visual analysis tool to segregate fat pads from other structures automatically. Results We developed a fully automatic segmentation technique that does not need any user interaction to extract fat pads. Our algorithm is fast enough that we can apply it to population-based epidemiological studies that provide a large amount of data. We evaluated our approach qualitatively on thirty datasets and quantitatively against the ground truths of ten datasets resulting in an average of approximately 78% detected volume fraction and a 79% Dice coefficient, which is within the range of the inter-observer variation of manual segmentation results. Conclusion The suggested method produces sufficiently accurate results and has potential to be applied for the study of large data to understand the pathogenesis of the OSA syndrome.