Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Accurate prostate segmentation in Trans Rectal Ultra Sound (TRUS) images is an important step in different clinical applications. Hover, the development of computer aided automatic prostate segmentation in TRUS images is a challenging task due to low contrast, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow, and speckle. Significant variations in prostate shape, size and contrast beten the datasets pose further challenges to achieve an accurate segmentation.   propose to use graph cuts in a Bayesian framework for automatic initialization and propagate multiple mean parametric models derived from principal component analysis of shape and posterior probability information of the prostate region to segment the prostate. The proposed framework achieves a mean Dice similarity coefficient value of 0.974±0.006, mean mean absolute distance value of 0.49±0.20 mm and mean Hausdorff distance of 1.24±0.56 mm when validated with 23 datasets in a leave-one-patient-out validation framework.
['Soumya Ghose', 'Arnau Oliver', 'Robert Marti', 'Xavier Lladó', 'Jordi Freixenet', 'Jhimli Mitra', 'Joan C. Vilanova', 'Josep Comet', 'Fabrice Meriaudeau']
I am writing a research paper on a idea Statistical Shape and Probability Prior Model for Automatic Prostate Segmentation
[{"from": "human", "value": "I am writing a research paper on a idea Statistical Shape and Probability Prior Model for Automatic Prostate Segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Accurate prostate segmentation in Trans Rectal Ultra Sound (TRUS) images is an important step in different clinical applications. Hover, the development of computer aided automatic prostate segmentation in TRUS images is a challenging task due to low contrast, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow, and speckle. Significant variations in prostate shape, size and contrast beten the datasets pose further challenges to achieve an accurate segmentation.   propose to use graph cuts in a Bayesian framework for automatic initialization and propagate multiple mean parametric models derived from principal component analysis of shape and posterior probability information of the prostate region to segment the prostate. The proposed framework achieves a mean Dice similarity coefficient value of 0.974\u00b10.006, mean mean absolute distance value of 0.49\u00b10.20 mm and mean Hausdorff distance of 1.24\u00b10.56 mm when validated with 23 datasets in a leave-one-patient-out validation framework."}]