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DOI: 10.1055/s-0031-1299376
Clinical Pilot Study for the Automatic Segmentation and Recognition of Abdominal Adipose Tissue Compartments from MRI Data
Klinische Pilotstudie für die automatische Segmentierung und Erkennung von abdominalem Fettgewebe in MRT-DatenPublikationsverlauf
15. November 2011
01. Februar 2012
Publikationsdatum:
20. März 2012 (online)
![](https://www.thieme-connect.de/media/roefo/201206/lookinside/thumbnails/10.1055-s-0031-1299376-1.jpg)
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.
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