Fully Automatic Segmentation of the Prostate using Active Appearance Models

We present a fully automatic model based system for segmenting the prostate in magnetic resonance (MR) images. The segmentation method is based on Active Appearance Models (AAM) built from manually segmented examples provided by the MICCAI 2012 Promise12 team. High quality correspondences for the model are generated using a Minimum Description Length (MDL) Groupwise Image Registration method. A multi start optimisation scheme is used to robustly match the model to new images. The model has been cross validated on the training data to a good degree of accuracy, and successfully segmented all the test data.

[1]  Alberto G. Ayala,et al.  Prostate pathology , 2004 .

[2]  Jocelyne Troccaz,et al.  Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model. , 2010, Medical physics.

[3]  Emmanouil Moschidis,et al.  Automatic differential segmentation of the prostate in 3-D MRI using Random Forest classification and graph-cuts optimization , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[4]  Timothy F. Cootes,et al.  Improving Appearance Model Matching Using Local Image Structure , 2003, IPMI.

[5]  Hans-Peter Meinzer,et al.  Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..

[6]  Olivier Clatz,et al.  Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery , 2007, NeuroImage.

[7]  P. Boyle,et al.  Serum prostate-specific antigen and prostate volume predict long-term changes in symptoms and flow rate: results of a four-year, randomized trial comparing finasteride versus placebo. PLESS Study Group. , 1999, Urology.

[8]  P. Kunz,et al.  A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[9]  R. Lenkinski,et al.  Accurate prostate volume estimation using multifeature active shape models on T2-weighted MRI. , 2011, Academic radiology.

[10]  Timothy F. Cootes,et al.  Groupwise Construction of Appearance Models using Piece-wise Affine Deformations , 2005, BMVC.

[11]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.