Authors:
Muhammad Laiq Ur Rahman Shahid
1
;
Teodora Chitiboi
2
;
Tatyana Ivanovska
3
;
Vladimir Molchanov
1
;
Henry Völzke
3
;
Horst K. Hahn
2
and
Lars Linsen
1
Affiliations:
1
Jacobs University, Germany
;
2
Jacobs University and Fraunhofer MEVIS, Germany
;
3
Ernst-Moritz-Arndt-Universität Greifswald, Germany
Keyword(s):
Obstructive Sleep Apnea (OSA), Pharynx Segmentation, Magnetic Resonance Imaging (MRI).
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Medical Image Applications
;
Segmentation and Grouping
Abstract:
Obstructive sleep apnea (OSA) is a public health problem. Volumetric analysis of the upper airways can help us to understand the pathogenesis of OSA. A reliable pharynx segmentation is the first step in identifying the anatomic risk factors for this sleeping disorder. As manual segmentation is a time-consuming and subjective process, a fully automatic segmentation of pharyngeal structures is required when investigating larger data
bases such as in cohort studies. We develop a context-based automatic algorithm for segmenting pharynx from magnetic resonance images (MRI). It consists of a pipeline of steps including pre-processing (thresholding, connected component analysis) to extract coarse 3D objects, classification of the objects (involving object-based image analysis (OBIA), visual feature space analysis, and silhouette coefficient computation) to segregate pharynx from other structures automatically, and post-processing to refine the shape of the identified pharynx (including ext
raction of the oropharynx and propagating results from neighboring slices to slices that are difficult to delineate). Our technique is fast such that we can apply our algorithm to population-based epidemiological studies that provide a high amount of data. Our method needs no user interaction to extract
the pharyngeal structure. The approach is quantitatively evaluated on ten datasets resulting in an average of approximately 90% detected volume fraction and a 90% Dice coefficient, which is in the range of the interobserver variation within manual segmentation results.
(More)