Topological MRI prostate segmentation method

The main aim of this paper is to advance the state of the art in automated prostate segmentation using T2 weighted MR images, by introducing a hybrid topological MRI prostate segmentation method which is based on a set of pre-labeled MR atlas images. The proposed method has been experimentally tested on a set of 30 MRI T2 weighted images. For evaluation the automated segmentations of the proposed scheme have been compared with the manual segmentations, using an average Dice Similarity Coefficient (DSC). Obtained quantitative results have shown a good approximation of the segmented prostate.

[1]  Yongyi Yang,et al.  Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI. , 2010, Medical physics.

[2]  Timothy F. Cootes,et al.  The Use of Active Shape Models for Locating Structures in Medical Images , 1993, IPMI.

[3]  G. Thomas,et al.  Semi-Automatic Prostate Segmentation of MR Images Based on Flow Orientation , 2006, 2006 IEEE International Symposium on Signal Processing and Information Technology.

[4]  N Betrouni,et al.  Zonal segmentation of prostate using multispectral magnetic resonance images. , 2011, Medical physics.

[5]  Yeong-Geon Seo,et al.  A TRUS Prostate Segmentation using Gabor Texture Features and Snake-like Contour , 2013, J. Inf. Process. Syst..

[6]  Aaron Fenster,et al.  Clinical application of a 3D ultrasound-guided prostate biopsy system. , 2011, Urologic oncology.

[7]  Timothy F. Cootes,et al.  Use of active shape models for locating structures in medical images , 1994, Image Vis. Comput..

[8]  Stefan Klein,et al.  Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. , 2008, Medical physics.

[9]  Josien P W Pluim,et al.  Multiatlas-based segmentation with preregistration atlas selection. , 2013, Medical physics.

[10]  Desire Sidibé,et al.  A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images , 2012, Comput. Methods Programs Biomed..

[11]  Christopher R. Porter,et al.  Prostate volume as an independent predictor of prostate cancer and high-grade disease on prostate needle biopsy , 2008 .

[12]  Dan Ruan,et al.  Low-complexity atlas-based prostate segmentation by combining global, regional, and local metrics. , 2014, Medical physics.

[13]  Fabrice Mériaudeau,et al.  A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance , 2011, 2011 18th IEEE International Conference on Image Processing.

[14]  Gabor Fichtinger,et al.  Prostate contouring in MRI guided biopsy , 2009, Medical Imaging.

[15]  Nacim Betrouni,et al.  Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary results , 2008, International Journal of Computer Assisted Radiology and Surgery.

[16]  L Lemaître,et al.  [Imaging of the prostate]. , 2006, Journal de radiologie.

[17]  Reyer Zwiggelaar,et al.  Semi-automatic Segmentation of the Prostate , 2003, IbPRIA.

[18]  Reyer Zwiggelaar,et al.  A hybrid ASM approach for sparse volumetric data segmentation , 2007, Pattern Recognition and Image Analysis.

[19]  Cecilia Sjöberg,et al.  Multi-atlas based segmentation using probabilistic label fusion with adaptive weighting of image similarity measures , 2013, Comput. Methods Programs Biomed..

[20]  Fabio Martínez,et al.  A novel atlas-based approach for MRI prostate segmentation using multiscale points of interest , 2013, Other Conferences.

[21]  W. Catalona,et al.  Measurement of prostate-specific antigen in serum as a screening test for prostate cancer. , 1991, The New England journal of medicine.