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