Journal of magnetic resonance imaging : JMRI · 2009
Accurate segmentation of subcutaneous and intermuscular adipose tissue from MR images of the thigh.
Оценочное исследованиеСтатьяНазвание и аннотация: Перевод в очереди
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Positano Vincenzo, Christiansen Tore, Santarelli Maria Filomena, Ringgaard Steffen, Landini Luigi, Gastaldelli Amalia
Аннотация
PURPOSE: To describe and evaluate a computer-assisted method for assessing the quantity and distribution of adipose tissue in thigh by magnetic resonance imaging (MRI).
MATERIALS AND METHODS: Twenty obese subjects were imaged on a Philips Achieva 1.5T scanner by a fast spin-echo (FSE) sequence. A total of 636 images were acquired and analyzed by custom-made software. Thigh subcutaneous adipose tissue (SAT) and bone were identified by fuzzy clustering segmentation and an active contour algorithm. Muscle and intermuscular adipose tissue (IMAT) were assessed by identifying the two peaks of the signal histogram with an expectation maximization algorithm. The whole analysis was performed in an unsupervised manner without the need of any user interaction.
RESULTS: The coefficient of variation (CV) was evaluated between the unsupervised algorithm and manual analysis performed by an expert operator. The CV was low for all measurements (SAT<2%, muscle<1%, IMAT<5%). Limited manual correction of unsupervised segmentation results (less than 10% of contours modified) allowed us to further reduce the CV (SAT<0.5%, muscle<0.5%, IMAT<2%).
CONCLUSION: The proposed approach allowed effective computer-assisted analysis of thigh MR images, dramatically reducing the user work compared to manual analysis. It allowed routine assessment of IMAT, a fat-depot linked with metabolic abnormalities, important in monitoring the effect of nutrition and exercise.
MATERIALS AND METHODS: Twenty obese subjects were imaged on a Philips Achieva 1.5T scanner by a fast spin-echo (FSE) sequence. A total of 636 images were acquired and analyzed by custom-made software. Thigh subcutaneous adipose tissue (SAT) and bone were identified by fuzzy clustering segmentation and an active contour algorithm. Muscle and intermuscular adipose tissue (IMAT) were assessed by identifying the two peaks of the signal histogram with an expectation maximization algorithm. The whole analysis was performed in an unsupervised manner without the need of any user interaction.
RESULTS: The coefficient of variation (CV) was evaluated between the unsupervised algorithm and manual analysis performed by an expert operator. The CV was low for all measurements (SAT<2%, muscle<1%, IMAT<5%). Limited manual correction of unsupervised segmentation results (less than 10% of contours modified) allowed us to further reduce the CV (SAT<0.5%, muscle<0.5%, IMAT<2%).
CONCLUSION: The proposed approach allowed effective computer-assisted analysis of thigh MR images, dramatically reducing the user work compared to manual analysis. It allowed routine assessment of IMAT, a fat-depot linked with metabolic abnormalities, important in monitoring the effect of nutrition and exercise.