J Digit Imaging - Anatomically anchored template-based level set segmentation: application to quadriceps muscles in MR images from the Osteoarthritis Initiative.

Tópicos

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Resumo

In this paper, we present a semi-automated segmentation method for magnetic resonance 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 as follows: rectus femoris, 0.78?0.12; vastus intermedius, 0.79?0.10; vastus lateralis, 0.82?0.08; and vastus medialis, 0.69?0.16.

Resumo Limpo

paper present semiautom segment method magnet reson imag quadricep muscl method use anatom anchor templatebas initi level setbas segment approach method requir input singl point user insid rectus femori templat quantit select set imag base mode patient popul name sex bodi type given imag segment templat select base smallest kullbackleibl diverg histogram imag set templat chosen templat employ initi level set segment captur individu anatom variat imag segment imag subject analyz use develop method algorithm train random select subset subject men women test remain subject perform algorithm test set compar ground truth use zijdenbo similar index zsi averag zsi mean standard deviat two differ manual reader follow rectus femori vastus intermedius vastus laterali vastus mediali

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