University of Heidelberg
Faculty of Medicine Mannheim
University Hospital Mannheim
CKM receives DFG funding for Project "Prdiktion von Therapieansprechen und Outcome beim lokal fortgeschrittenen Rektum-Karzinom mittels Radiomics und Deep Learning: eine beispielhafte Anwendung fr eine allgemein verwendbare, Deep Learning basierte Prozessierungs-Pipeline fr 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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{A}utomatic {S}egmentation of {U}nstained {L}iving {C}ells in {B}right-{F}ield {M}icroscope {I}mages

M. Tscherepanow, F. Zöllner and F. Kummert

Workshop on Mass-Data Analysis of Images and Signals in Medicine, Biotechnology and Chemistry MDA,, pp.86-95

The automatic subcellular localisation of proteins in living cells is a critical step to determine their function. The evaluation of fluorescence images constitutes a common method of localising these proteins. For this, additional knowledge about the position of the considered cells within an image is required. In an automated system, it is advantageous to locate and segment these cells in bright-field microscope images taken in parallel with the fluorescence micrographs. Unfortunately, currently available cell segmentation methods are only of limited use within the context of protein localisation, since they frequently require microscopy techniques that enable images of higher contrast (e.g. phase contrast microscopy or additional dyes) or can merely be employed with too small magnifications. Therefore, this article introduces a novel approach for the robust automatic segmentation of unstained living cells in bright-field microscope images.

Contact: Prof. Dr. Frank Zöllner last modified: 21.09.2020
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