Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for...

Area of Science:

• Medical Imaging
• Sleep Medicine
• Anatomy

Background:

• Obstructive sleep apnea (OSA) is a significant public health concern.
• Understanding the pathogenesis of OSA requires detailed analysis of parapharyngeal fat pads.
• Identifying anatomical risk factors for OSA necessitates reliable segmentation techniques for these fat pads.

Purpose of the Study:

• To develop a context-based automatic segmentation algorithm for parapharyngeal fat pads.
• To delineate fat pads from magnetic resonance images (MRIs) in a population-based study.
• To enable automated segregation of fat pads from other anatomical structures.

Main Methods:

• A segmentation pipeline integrating texture analysis, connected component analysis, and object-based image analysis.
• Supervised classification employing an interactive visual analysis tool for automated segregation.
• Development of a fully automatic segmentation technique requiring no user interaction.

Main Results:

• The developed algorithm automatically extracts parapharyngeal fat pads efficiently.
• The method is suitable for large-scale population-based epidemiological studies.
• Quantitative evaluation yielded an average 78% detected volume fraction and 79% Dice coefficient, comparable to inter-observer variability.

Conclusions:

• The automatic segmentation method provides accurate results for parapharyngeal fat pads.
• This technique holds potential for large-scale studies investigating OSA pathogenesis.
• The findings contribute to understanding anatomical risk factors in OSA syndrome.