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Automatic prostate segmentation in MR images with a probabilistic active shape model

Presentation held at PROMISE 2012, MICCAI 2012 Grand Challenge: Prostate MR Image Segmentation 2012, October 1, 2012, Nice, France
: Kirschner, Matthias; Jung, Florian; Wesarg, Stefan

Fulltext (PDF; )

2012, 8 pp.
"Prostate MR Image Segmentation" Challenge (PROMISE) <2012, Nice>
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) <15, 2012, Nice>
Conference Paper, Electronic Publication
Fraunhofer IGD ()
active shape model (ASM); segmentation; object detection; magnetic resonance imaging (MRI); Forschungsgruppe Medical Computing (MECO)

Segmentation of the prostate gland in Magnetic Resonance (MR) images is an important task for image-guided prostate cancer therapy. The low contrast of the prostate to surrounding tissue in MR images makes automatic segmentation very challenging. In this paper, we propose an automatic approach for robust and accurate prostate segmentation in T2-weighted MR scans. We first employ a boosted prostate detector to locate the prostate in the images, and then use a Probabilistic Active Shape Model for the delineation of its contour. Our approach has been quantitatively evaluated on 50 MR images, on which we achieve a median dice coefficient of 0.85 (IQR: 0.09).