title: Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Jacobs University, Bremen, Germany. m.shahid@jacobs-university.de.
authors.forename: Muhammad Laiq Ur Rahman
authors.identifier: 0000-0002-5223-4744
authors.initials: ML
authors.lastname: Shahid
authors.affiliation: Jacobs University, Bremen, Germany.|Fraunhofer MEVIS, Bremen, Germany.
authors.forename: Teodora
authors.initials: T
authors.lastname: Chitiboi
authors.affiliation: Georg-August-Universität, Göttingen, Germany.
authors.forename: Tetyana
authors.initials: T
authors.lastname: Ivanovska
authors.affiliation: Jacobs University, Bremen, Germany.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Molchanov
authors.affiliation: Ernst-Moritz-Arndt-Universität, Greifswald, Germany.
authors.forename: Henry
authors.initials: H
authors.lastname: Völzke
authors.affiliation: Jacobs University, Bremen, Germany.
authors.forename: Lars
authors.initials: L
authors.lastname: Linsen
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D010614:Pharynx / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020181:Sleep Apnea, Obstructive / Q000000981:diagnostic imaging*; D014584:User-Computer Interface; D055815:Young Adult
keywords: Interactive visual analysis tool; Magnetic resonance imaging (MRI); Obstructive sleep apnea (OSA); Para-pharyngeal fat pads segmentation; Upper airway segmentation