Prostate segmentation based on variant scale patch and local independent projection

Accurate segmentation of the prostate in computed tomography (CT) images is important in image-guided radiotherapy; however, difficulties remain associated with this task. In this study, an automatic framework is designed for prostate segmentation in CT images. We propose a novel image feature extraction method, namely, variant scale patch, which can provide rich image information in a low dimensional feature space. We assume that the samples from different classes lie on different nonlinear submanifolds and design a new segmentation criterion called local independent projection (LIP). In our method, a dictionary containing training samples is constructed. To utilize the latest image information, we use an online updated strategy to construct this dictionary. In the proposed LIP, locality is emphasized rather than sparsity; local anchor embedding is performed to determine the dictionary coefficients. Several morphological operations are performed to improve the achieved results. The proposed method has been evaluated based on 330 3-D images of 24 patients. Results show that the proposed method is robust and effective in segmenting prostate in CT images.

Automatic 3D segmentation of prostate in MRI combining a priori knowledge, Markov fields and Bayesian framework

Accurate localization and contouring of prostate are important issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. Our paper introduces an original method for automatic 3D segmentation of the prostate gland from Magnetic Resonance Imaging data. We use a statistical shape model as a priori knowledge, and we model gray levels distribution by fitting histogram modes with a Gaussian mixture. Markov fields are used to introduce contextual information regarding voxels neighbourhood. Final labelling optimization is based on Bayesian a posteriori classification, estimated with the Iterative Conditional Mode algorithm (ICM). We compared the accuracy of this method, free from any manual correction, with contours outlined by an expert radiologist. In 6 random cases, including prostates with cancer and benign prostatic hypertrophy (BPH), mean Hausdorff distance (HD) and Overlap Ratio (OR) were 9.94 mm and 0.83, respectively. Beyond fast computing times, this new method showed satisfying results, even at prostate's base and apex.

Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI

PURPOSE

Accurate localization and contouring of prostate are crucial issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. We introduce a new method for automatic 3D segmentation of the prostate gland from magnetic resonance imaging (MRI) scans.

METHODS

A statistical shape model was used as an a priori knowledge, and gray levels distribution was modeled by fitting histogram modes with a Gaussian mixture. Markov fields were used to introduce contextual information regarding voxels' neighborhoods. Final labeling optimization is based on Bayesian a posteriori classification, estimated with the iterative conditional mode algorithm.

RESULTS

We compared the accuracy of this method, free from any manual correction, with contours outlined by an expert radiologist. In 12 cases, including prostates with cancer and benign prostatic hypertrophy, the mean Hausdorff distance and overlap ratio were 9.94 mm and 0.83, respectively.

CONCLUSION

This new automatic prostate MRI segmentation method produces satisfactory results, even at prostate's base and apex. The method is computationally feasible and efficient.