Towards renal compartment segmentation using an unsupervised neural network approach
نویسندگان
چکیده
Introduction Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is an emerging technique for a more accurate assessment of local renal function [1]. Analysis of DCE-MRI time series is typically based on manual delineation of regions of interest in the recorded images. However, such procedures are time consuming, expensive and error prone [2,3], and subject to intraand interobserver variations. Automated methods mostly involves user interaction [4,5] or are based on model assumptions [6]. In this work we present a model free and unsupervised approach to renal compartment segmentation in 3D DCE-MRI data. Thereby self organizing maps (SOM) are utilized [7].
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