An Adipose Segmentation and Quantification Scheme for the Abdominal Region in Minipigs

Rasmus Engholm, Aleks Dubinskiy, Rasmus Larsen, Lars G. Hanson, Berit Østergaard Christoffersen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    This article describes a method for automatic segmentation of the abdomen into three anatomical regions: subcutaneous, retroperitoneal and visceral. For the last two regions the amount of adipose tissue (fat) is quantified. According to recent medical research, the distinction between retroperitoneal and visceral fat is important for studying metabolic syndrome, which is closely related to diabetes.1 However previous work has neglected to address this point, treating the two types of fat together. We use T1-weighted three-dimensional magnetic resonance data of the abdomen of obese minipigs. The pigs were manually dissected right after the scan, to produce the “ground truth” segmentation. We perform automatic segmentation on a representative slice, which on humans has been shown to correlate with the amount of adipose tissue in the abdomen. The process of automatic fat estimation consists of three steps. First, the subcutaneous fat is removed with a modified active contour approach. The energy formulation of the active contour exploits the homogeneous nature of the subcutaneous fat and the smoothness of the boundary. Subsequently the retroperitoneal fat located around the abdominal cavity is separated from the visceral fat. For this, we formulate a cost function on a contour, based on intensities, edges, distance to center and smoothness, so as to exploit the properties of the retroperitoneal fat. We then globally optimize this function using dynamic programming. Finally, the fat content of the retroperitoneal and visceral regions is quantified based on a fuzzy c-means classification of the intensities within the segmented regions. The segmentation proved satisfactory by visual inspection, and closely correlated with the manual dissection data. The correlation was 0.84 for the retroperitoneal fat, and 0.76 for the visceral fat.
    Original languageEnglish
    Title of host publicationInternational Symposium on Medical Imaging 2006, San Diego, CA, USA
    PublisherSPIE - International Society for Optical Engineering
    Publication date2006
    Publication statusPublished - 2006
    Event2006 International Symposium on Medical Imaging - San Diego, CA, United States
    Duration: 11 Feb 200616 Feb 2006


    Conference2006 International Symposium on Medical Imaging
    Country/TerritoryUnited States
    CitySan Diego, CA


    • Snake
    • Abdominal
    • Segmentation
    • Deformable model
    • MR

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