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Liver segmentation in contrast enhanced MR datasets using a probabilistic active shape and appearance model

: Drechsler, Klaus; Knaub, Anton; Oyarzun Laura, Cristina; Wesarg, Stefan


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 27th International Symposium on Computer-Based Medical Systems, CBMS 2014 : New York, New York, USA, 27 - 29 May 2014
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2014
ISBN: 978-1-4799-4435-4
ISBN: 978-1-4799-4434-7
International Symposium on Computer-Based Medical Systems (CBMS) <27, 2014, New York/NY>
Fraunhofer IGD ()
image segmentation; liver; Tumors; computed tomography (CT); magnetic resonance imaging (MRI); medical imaging; medical image processing; medical modeling; medical diagnosis

The current standard for diagnosing liver tumors is contrast-enhanced multiphase computed tomography. On this basis, several software tools have been developed by different research groups worldwide to support physicians for example in measuring remnant liver volume, analyzing tumors, and planning resections. Several algorithms have been developed to perform these tasks. Most of the time, the segmentation of the liver is at the beginning of the processing chain. Therefore, a vast amount of CT-based liver segmentation algorithms have been developed. However, clinics slowly move from CT as the current gold standard for diagnosing liver diseases towards magnetic resonance imaging. In this work, we utilize a Probabilistic Active Shape Model with an MR specific preprocessing and appearance model to segment the liver in contrast enhanced MR images. Evaluation is based on 8 clinical datasets.