A hybrid framework of multiple active appearance models and global registration for 3D prostate segmentation in MRI

Soumya Ghose; Arnau Oliver; Robert Martí; Xavier Lladó; Jordi Freixenet; Jhimli Mitra; Joan C. Vilanova; Fabrice Meriaudeau

doi:10.1117/12.911253

14 February 2012 A hybrid framework of multiple active appearance models and global registration for 3D prostate segmentation in MRI

Soumya Ghose, Arnau Oliver, Robert Martí, Xavier Lladó, Jordi Freixenet, Jhimli Mitra, Joan C. Vilanova, Fabrice Meriaudeau

Author Affiliations +

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83140S (2012) https://doi.org/10.1117/12.911253
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Real-time fusion of Magnetic Resonance (MR) and Trans Rectal Ultra Sound (TRUS) images aid in the localization of malignant tissues in TRUS guided prostate biopsy. Registration performed on segmented contours of the prostate reduces computational complexity and improves the multimodal registration accuracy. However, accurate and computationally efficient 3D segmentation of the prostate in MR images could be a challenging task due to inter-patient shape and intensity variability of the prostate gland. In this work, we propose to use multiple statistical shape and appearance models to segment the prostate in 2D and a global registration framework to impose shape restriction in 3D. Multiple mean parametric models of the shape and appearance corresponding to the apex, central and base regions of the prostate gland are derived from principal component analysis (PCA) of prior shape and intensity information of the prostate from the training data. The estimated parameters are then modified with the prior knowledge of the optimization space to achieve segmentation in 2D. The 2D segmented slices are then rigidly registered with the average 3D model produced by affine registration of the ground truth of the training datasets to minimize pose variations and impose 3D shape restriction. The proposed method achieves a mean Dice similarity coefficient (DSC) value of 0.88±0.11, and mean Hausdorff distance (HD) of 3.38±2.81 mm when validated with 15 prostate volumes of a public dataset in leave-one-out validation framework. The results achieved are better compared to some of the works in the literature.

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Soumya Ghose, Arnau Oliver, Robert Martí, Xavier Lladó, Jordi Freixenet, Jhimli Mitra, Joan C. Vilanova, and Fabrice Meriaudeau "A hybrid framework of multiple active appearance models and global registration for 3D prostate segmentation in MRI", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140S (14 February 2012); https://doi.org/10.1117/12.911253

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