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Towards renal compartment segmentation using an unsupervised neural network approach

F. Zöllner and L. Schad

Proceedings 17th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Honolulu,, p.4138

Dynamic contrast enhanced magnetic resonance imaging is an emerging technique for a more accurate assessment of local renal function. Automated methods mostly involves user interaction or are based on model assumptions.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. Initial results demonstrate that SOMs could be used for a segmentation of the renal compartments but also, could give qualitative insights into local perfusion patterns of the kidney.

Contact: Prof. Dr. Frank Zöllner last modified: 30.11.2020
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