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A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance

Abstract : Prostate volume estimation from segmented prostate contours in Trans Rectal Ultrasound (TRUS) images aids in diagnosis and treatment of prostate diseases, including prostate cancer. However, accurate, computationally efficient and automatic segmentation of the prostate in TRUS images is a challenging task owing to low Signal-To-Noise-Ratio (SNR), speckle noise, micro-calcifications and heterogeneous intensity distribution inside the prostate region. In this paper, we propose a probabilistic framework for propagation of a parametric model derived from Principal Component Analysis (PCA) of prior shape and posterior probability values to achieve the prostate segmentation. The proposed method achieves a mean Dice similarity coefficient value of 0.96±0.01, and a mean absolute distance value of 0.80±0.24 mm when validated with 24 images from 6 datasets in a leave-one-patient-out validation framework. Our proposed model is automatic, and performs accurate prostate segmentation in presence of intensity heterogeneity and imaging artifacts.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00629085
Contributor : Fabrice Meriaudeau <>
Submitted on : Wednesday, October 5, 2011 - 7:54:51 AM
Last modification on : Friday, July 17, 2020 - 2:54:03 PM

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  • HAL Id : hal-00629085, version 1

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Soumya Ghose, Arnau Oliver, Robert Marti, Xavier Llado, Jordi Freixenet, et al.. A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance. 18th IEEE International Conference on Image Processing, Sep 2011, Bruxelles, Belgium. pp.725-728. ⟨hal-00629085⟩

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