Prostate segmentation from 2D ultrasound images

Ladak, Hanif M.; Mao, Fei; Wang, Yunqiu; Downey, Dónal B.; Steinman, David A.; Fenster, Aaron

doi:10.1109/iembs.2000.901572

Cited by 69 publications

(42 citation statements)

References 11 publications

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“…The average DSC value of our method with 0.95±0.01 is comparable to mean DSC values achieved by Betrouni et al 7 and Ladak et al 17 and inferior to the area overlap measure of Abolmaesumi et al 16 of 98%. However, it is to be noted that we have used more images (24 images) compared to 6 images of Abolmaesumi et al Our MAD value of 1.50±0.41 mm is comparable to Gong et al…”

Section: Resultssupporting

confidence: 70%

Prostate segmentation with local binary patterns guided active appearance models

et al. 2011

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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 segmentation of the prostate in TRUS images could be challenging in the presence of heterogeneous intensity distribution inside the prostate gland, and other imaging artifacts like speckle noise, shadow regions and low Signal to Noise Ratio (SNR). In this work, we propose to enhance the texture features of the prostate region using Local Binary Patterns (LBP) for the propagation of a shape and appearance based statistical model to segment the prostate in a multi-resolution framework. A parametric model of the propagating contour is derived from Principal Component Analysis (PCA) of the prior shape and texture information of the prostate from the training data. The estimated parameters are then modified with the prior knowledge of the optimization space to achieve an optimal segmentation. The proposed method achieves a mean Dice Similarity Coefficient (DSC) value of 0.94±0.01 and a mean segmentation time of 0.68±0.02 seconds when validated with 70 TRUS images of 7 datasets in a leave-one-patient-out validation framework. Our method performs computationally efficient and accurate prostate segmentation in the presence of intensity heterogeneities and imaging artifacts.

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Section: Resultssupporting

confidence: 70%

Prostate segmentation with local binary patterns guided active appearance models

et al. 2011

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“…Analyzing the results, we observe that our mean DSC value is better than the area overlap accuracy values of Betrouni et al [3] and Ladak et al [14] and very similar to the area overlap error of Shen et al [18]. However, it is to be noted that we have used more images compared to Shen et al Our MAD value also shows improvement when compared to [3], [18], [14], [6] and [19]. From these observations we may infer our method performs well in overlap and contour accuracy measures when assessed qualitatively.…”

Section: Resultsmentioning

confidence: 76%

“…For example Badiei et al [2] used a deformable model of warping ellipse and Ladak et al [14] used discrete dynamic contour to achieve semi-automatic prostate segmentation. However, prostate segmentation during TRUS guided biopsy procedures should necessarily be automatic.…”

Section: Introductionmentioning

confidence: 99%

A Supervised Learning Framework for Automatic Prostate Segmentation in Trans Rectal Ultrasound Images

Ghose

Mitra

Oliver

et al. 2012

Advanced Concepts for Intelligent Vision Systems

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Abstract. Heterogeneous intensity distribution inside the prostate gland, significant variations in prostate shape, size, inter dataset contrast variations, and imaging artifacts like shadow regions and speckle in Trans Rectal Ultrasound (TRUS) images challenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a supervised learning schema based on random forest for automatic initialization and propagation of statistical shape and appearance model. Parametric representation of the statistical model of shape and appearance is derived from principal component analysis (PCA) of the probability distribution inside the prostate and PCA of the contour landmarks obtained from the training images. Unlike traditional statistical models of shape and intensity priors, the appearance model in this paper is derived from the posterior probabilities obtained from random forest classification. This probabilistic information is then used for the initialization and propagation of the statistical model. The proposed method achieves mean Dice Similarity Coefficient (DSC) value of 0.96±0.01, with a mean segmentation time of 0.67±0.02 seconds when validated with 24 images from 6 datasets with considerable shape, size, and intensity variations, in a leave-one-patient-out validation framework. The model achieves statistically significant t-test p-value<0.0001 in mean DSC and mean mean absolute distance (MAD) values compared to traditional statistical models of shape and intensity priors.

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“…These methods require expert intervention during initialization of the model or during refinement of the segmented contour. Often deformable models and statistical shape models are used to achieve segmentation in semi automatic methods like [3], [4]. However, it is necessary to use an automatic prostate segmentation method for TRUS guided procedures.…”

Section: Introductionmentioning

confidence: 99%

Statistical Shape and Probability Prior Model for Automatic Prostate Segmentation

Ghose

Oliver

Martí

et al. 2011

2011 International Conference on Digital Image Computing: Techniques and Applications

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Abstract-Accurate prostate segmentation in Trans RectalUltra Sound (TRUS) images is an important step in different clinical applications. However, 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 between the datasets pose further challenges to achieve an accurate segmentation. In this paper we 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.

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Prostate segmentation from 2D ultrasound images

Cited by 69 publications

References 11 publications

Prostate segmentation with local binary patterns guided active appearance models

Prostate segmentation with local binary patterns guided active appearance models

A Supervised Learning Framework for Automatic Prostate Segmentation in Trans Rectal Ultrasound Images

Statistical Shape and Probability Prior Model for Automatic Prostate Segmentation

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