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

Kirschner, Matthias ; Jung, Florian ; Wesarg, Stefan (2012)
Automatic Prostate Segmentation in MR Images with a Probabilistic Active Shape Model.
PROMISE12. Proceedings.
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (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).

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2012
Autor(en): Kirschner, Matthias ; Jung, Florian ; Wesarg, Stefan
Art des Eintrags: Bibliographie
Titel: Automatic Prostate Segmentation in MR Images with a Probabilistic Active Shape Model
Sprache: Englisch
Publikationsjahr: 2012
Veranstaltungstitel: PROMISE12. Proceedings
Kurzbeschreibung (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).

Freie Schlagworte: 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)
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 12 Nov 2018 11:16
Letzte Änderung: 12 Nov 2018 11:16
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