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Open Access 01-12-2017 | Research Article

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

Authors: Muhammad Laiq Ur Rahman Shahid, Teodora Chitiboi, Tetyana Ivanovska, Vladimir Molchanov, Henry Völzke, Lars Linsen

Published in: BMC Medical Imaging | Issue 1/2017

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.

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Title: Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification
Authors: Muhammad Laiq Ur Rahman Shahid
Teodora Chitiboi
Tetyana Ivanovska
Vladimir Molchanov
Henry Völzke
Lars Linsen
Publication date: 01-12-2017
Publisher: BioMed Central
Published in: BMC Medical Imaging / Issue 1/2017
Electronic ISSN: 1471-2342
DOI: https://doi.org/10.1186/s12880-017-0179-7

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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