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OAI quadriceps segmentation work accepted for publication in the Journal of Digital Imaging

A method for the segmentation of the quadriceps muscles in OAI images developed by the CIALAB Osteoarthritis Research Group has been accepted for publication in the Journal of Digital Imaging. The authors of the paper were Jeff Prescott, an MD/PhD student in the lab, Thomas Best MD/PhD, Mark Swanson, Furqan Haq PhD, Rebecca Jackson MD, and Metin Gurcan PhD. The abstract of the manuscript appears below.

Abstract: In this paper we present a semi-automated segmentation method for magnetic resonance (MR) images of the quadriceps muscles. Our method uses an anatomically anchored, template-based initialization of the level set-based segmentation approach. The method only requires the input of a single point from the user inside the rectus femoris. The templates are quantitatively selected from a set of images based on modes in the patient population, namely sex and body type. For a given image to be segmented, a template is selected based on the smallest Kullback-Leibler divergence between the histograms of that image and the set of templates. The chosen template is then employed as an initialization for a level set segmentation, which captures individual anatomical variations in the image to be segmented. Images from 103 subjects were analyzed using the developed method. The algorithm was trained on a randomly selected subset of 50 subjects (25 men and 25 women) and tested on the remaining 53 subjects. The performance of the algorithm on the test set was compared against the ground truth using the Zijdenbos similarity index (ZSI). The average ZSI means and standard deviations against two different manual readers were: rectus femoris, 0.78 ± 0.12; vastus intermedius, 0.79 ± 0.10; vastus lateralis, 0.82 ± 0.08; vastus medialis, 0.69 ± 0.16.

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