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

Muhammad Laiq Ur Rahman Shahid, Teodora Chitiboi, Tetyana Ivanovska, Vladimir Molchanov, Henry Völzke, Lars Linsen
<span title="2017-02-14">2017</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/65e5jydmjzhjbldqtsyy7j5wia" style="color: black;">BMC Medical Imaging</a> </i> &nbsp;
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12880-017-0179-7">doi:10.1186/s12880-017-0179-7</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28196476">pmid:28196476</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5309996/">pmcid:PMC5309996</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uhzk5acnv5gxdk5wecrequjeqa">fatcat:uhzk5acnv5gxdk5wecrequjeqa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191029114937/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5309996&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ed/fd/edfd572866544c1c0dc1c7e095f4a5947e4d61de.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12880-017-0179-7"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5309996" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>