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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Tumor Tissue Analysis by Self Organizing Maps from combined DCE-/DSC-MRI data

F. Zöllner, M. Heilmann, C. Walczak, A. Volk and L. Schad

Proceedings of the 6th International Symposium on Image ans Signal Processing and Analysis,, pp.562-568

Self organizing maps are utilized to analyze tumor tissue from simultaneously acquired dynamic T1 and T2* magnetic resonance imaging measurements of five tumor-bearing mice. The method allowed for tumor segmention obtaining regions characterized by distinct perfusion patterns (i.e. T1 and T2* perfusion time curves). Compared to histopathological analysis, these regions comprise typical tumor areas like pre-necrotic or vascularized tissue. Furthermore, the detected regions showed differences in physiological parameters (Ktrans, ve) extracted by a pharmacokinetic model. In summary, tumor tissue analysis by self organizing maps is feasible and seems to be a valuable tool in model-free assessment of tumor physiology.

Contact: Prof. Dr. Frank Zöllner last modified: 01.07.2020
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