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Automatic Prostate Segmentation in MR Images with a Probabilistic Active Shape Model

Kirschner, Matthias and Jung, Florian and Wesarg, Stefan (2012):
Automatic Prostate Segmentation in MR Images with a Probabilistic Active Shape Model.
pp. 28-35, PROMISE12. Proceedings, [Conference or Workshop Item]

Abstract

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).

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Kirschner, Matthias and Jung, Florian and Wesarg, Stefan
Title: Automatic Prostate Segmentation in MR Images with a Probabilistic Active Shape Model
Language: English
Abstract:

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).

Uncontrolled Keywords: Forschungsgruppe Medical Computing (MECO), Business Field: Visual decision support, Research Area: Confluence of graphics and vision, Active shape models (ASM), Segmentation, Object detection, Magnetic resonance imaging (MRI)
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: PROMISE12. Proceedings
Date Deposited: 12 Nov 2018 11:16
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