Clinical Pilot Study for the Automatic Segmentation and Recognition of Abdominal Adipose Tissue Compartments from MRI Data

Abstract

Purpose: In the diagnosis and risk assessment of obesity, both the amount and distribution of adipose tissue compartments are critical factors. We present a hybrid method for the quantitative measurement of human body fat compartments.

Materials and Methods: MRI imaging was performed on a 1.5 T scanner. In a pre-processing step, the images were corrected for bias field inhomogeneity. For segmentation and recognition a hybrid algorithm was developed to automatically differentiate between different adipose tissue compartments. The presented algorithm is designed with a combination of shape and intensity-based techniques. To incorporate the presented algorithm into the clinical routine, we developed a graphical user interface. Results from our methods were compared with the known volume of an adipose tissue phantom. To evaluate our method, we analyzed 40 clinical MRI scans of the abdominal region.

Results: Relatively low segmentation errors were found for subcutaneous adipose tissue (3.56 %) and visceral adipose tissue (0.29 %) in phantom studies. The clinical results indicated high correlations between the distribution of adipose tissue compartments and obesity.

Conclusion: We present an approach that rapidly identifies and quantifies adipose tissue depots of interest. With this method examination and analysis can be performed in a clinically feasible timeframe.

Zusammenfassung

Ziel: In der Diagnose und Risikoabschätzung von Adipositas sind sowohl die Menge als auch die Verteilung von Fettgewebe kritische Faktoren. Wir präsentieren ein hybrides Verfahren zur quantitativen Messung der menschlichen Körperfettverteilung.

Material und Methoden: Die MRT-Bildgebung wurde an einem 1,5T-Scanner durchgeführt. In einem Datenvorbehandlungsschritt wurden in jedem Datensatz die Bias-Field-Inhomogenität korrigiert. Für die Segmentierung und Erkennung der Körperfettverteilung wurde ein Algorithmus entwickelt, um automatisch zwischen den verschiedenen Fettgeweben zu differenzieren. Zur Integration des Algorithmus in der klinischen Routine ist ein Graphical-User-Interface entwickelt worden. Die Genauigkeit unserer Verfahren ist mit Zuhilfenahme eines digitalen Fettgewebephantoms bestimmt worden. Des Weiteren sind zur Evaluation des Verfahrens 40 klinische MRI-Scans des Abdomens analysiert worden.

Ergebnisse: Relativ geringe Segmentierungsfehler wurden für das subkutane Fettgewebe (3,56 %) und viszerale Fettgewebe (0,29 %) in dieser Phantomstudie bestimmt. Die Analyse der klinischen Daten führte zu dem Ergebnis, dass es hohe Korrelationen zwischen der Verteilung des Fettgewebes und Adipositas gibt.

Schlussfolgerung: Wir präsentieren ein Verfahren, welches verlässlich die Fettgewebeverteilung identifiziert und quantifiziert. Mit diesem Verfahren ermöglichen wir in einem klinisch machbaren Zeitrahmen die Untersuchung und Analyse der Fettgewebeverteilung.

Key words

MR imaging - segmentation - physics - image manipulation/reconstruction

• References

• 1 Mathers CD, Loncar D. Updated projections of global mortality and burden of disease, 2002–2030: data sources, methods and results. Evidence and Information for Policy Working Paper Evidence and Information for Policy Working Paper 2005
• 2 WHO Committee. Physical status: The use and interpretation of Anthropometry. WHO Technical Report Series; 1995: 854
• 3 Taylor G. Good Fat, Bad Fat – Does Location Matter?. Radiology 2007; 242: 645-646
• 4 Kullberg J et al. Whole-body adipose tissue analysis: comparison of MRI, CT and dual energy X-ray absorptiometry. Br J Radiol 2009; 82: 123-130
• 5 Maurovich-Horvat P et al. Comparison of anthropometric, area- and volume-based assessment of abdominal subcutaneous and visceral adipose tissue volumes using multi-detector computed tomography. Int J Obes 2007; 31: 500-506
• 6 Yang GZ et al. Automatic MRI adipose tissue mapping using overlapping mosaics. MAGMA 2002; 14: 39-44
• 7 Al-Attar SA et al. Semi-automated segmentation and quantification of adipose tissue in calf and thigh by MRI: a preliminary study in patients with monogenic metabolic syndrome. BMC Med Imaging 2006; 6: 11
• 8 Kullberg J et al. Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI. Int J Obes 2007; 31: 1806-1817
• 9 Jørgensen PS et al. Unsupervised Assessment of Subcutaneous and Visceral Fat by MRI, in Proceedings of the 16th Scandinavian Conference on Image Analysis. Oslo, Norway: Springer-Verlag; 2009: 179-188
• 10 Positano V et al. An accurate and robust method for unsupervised assessment of abdominal fat by MRI. Journal of Magnetic Resonance Imaging 2004; 20: 684-689
• 11 Jin YP et al. Segmentation and evaluation of adipose tissue from whole body MRI scans. Medical Image Computing and Computer-Assisted Intervention – Miccai 2003; 2878: 635-642
• 12 Bonekamp S et al. Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging. Int J Obes 2008; 32: 100-111
• 13 Würslin C et al. Topography mapping of whole body adipose tissue using. A fully automated and standardized procedure. J Magn Reson Imaging 2010; 31: 430-439
• 14 Chen Y, Zhang J, Macione J. An improved level set method for brain MR images segmentation and bias correction. Comput Med Imaging Graph 2009; 33: 510-519
• 15 Chua ZY et al. Evaluation of performance metrics for bias field correction in MR brain images. J Magn Reson Imaging 2009; 29: 1271-1279
• 16 Fan A et al. A unified variational approach to denoising and bias correction in MR. Inf Process Med Imaging 2003; 18: 148-159
• 17 Gispert JD et al. Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error. Hum Brain Mapp 2004; 22: 133-144
• 18 Ji Q, Glass JO, Reddick WE. A novel, fast entropy-minimization algorithm for bias field correction in MR images. Magn Reson Imaging 2007; 25: 259-64
• 19 Lai SH, Fang M. A dual image approach for bias field correction in magnetic resonance imaging. Magn Reson Imaging 2003; 21: 121-125
• 20 Learned-Miller EG, Jain V. Many heads are better than one: jointly removing bias from multiple MRIs using nonparametric maximum likelihood. Inf Process Med Imaging 2005; 19: 615-626
• 21 Luo J et al. Correction of bias field in MR images using singularity function analysis. IEEE Trans Med Imaging 2005; 24: 1067-1085
• 22 Milchenko MV, Pianykh OS, Tyler JM. The fast automatic algorithm for correction of MR bias field. J Magn Reson Imaging 2006; 24: 891-900
• 23 Van Leemput K et al. Automated model-based bias field correction of MR images of the brain. IEEE Trans Med Imaging 1999; 18: 885-896
• 24 Bricq S, Collet C, Armspach JP. Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains. Med Image Anal 2008; 12: 639-652
• 25 Studholme C et al. Accurate template-based correction of brain MRI intensity distortion with application to dementia and aging. IEEE Trans Med Imaging 2004; 23: 99-110
• 26 Van de Moortele PF et al. T1 weighted brain images at 7 Tesla unbiased for Proton Density, T2* contrast and RF coil receive B1 sensitivity with simultaneous vessel visualization. Neuroimage 2009; 46: 432-446
• 27 Zhou Y, Bai J. Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI. IEEE Trans Biomed Eng 2007; 54: 122-129
• 28 Salvado O et al. Method to correct intensity inhomogeneity in MR images for atherosclerosis characterization. IEEE Trans Med Imaging 2006; 25: 539-552
• 29 Ibáñez L et al. The ITK Software Guide. 2003
• 30 Kass M, Witkin A, Terzopoulos D. Snakes – Active Contour Models. International Journal of Computer Vision 1987; 1: 321-331
• 31 Xu CY, Prince JL. Snakes, shapes, and gradient vector flow. Ieee Transactions on Image Processing 1998; 7: 359-369
• 32 Rasband W. ImageJ. U. S. National Institutes of Health, Bethesda, Maryland, USA. , 1997–2009. http://rsb.info.nih.gov/ij/
• 33 Staten MA, Totty WG, Kohrt WM. Measurement of Fat Distribution by Magnetic-Resonance Imaging. Investigative Radiology 1989; 24: 345-349
• 34 Elbers JMH et al. Reproducibility of fat area measurements in young, non-obese subjects by computerized analysis of magnetic resonance images. International Journal of Obesity 1997; 21: 1121-1129