University of Heidelberg
Faculty of Medicine Mannheim
University Hospital Mannheim
CKM receives DFG funding for Project "Prädiktion von Therapieansprechen und Outcome beim lokal fortgeschrittenen Rektum-Karzinom mittels Radiomics und Deep Learning: eine beispielhafte Anwendung für eine allgemein verwendbare, Deep Learning basierte Prozessierungs-Pipeline für die Bild-Klassifikation" read more.
Alena-Kathrin Schnurr presented on our research on AI in medical imaging @ Research Plus forum
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Dr. Mathias Davids wins the I. I. Rabi Award of the International Society for Magnetic Resonance in Medicine (ISMRM),
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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: 01.07.2020
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