Segmenting ultrasound images of the prostate using neural networks

Prater, J.S.

doi:10.1016/0161-7346(92)90005-g

Cited by 35 publications

(24 citation statements)

References 9 publications

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“…Prater et al reported an approach 6 for segmenting transrectal US images of the prostate by virtue of pixel-by-pixel classification of the prostatic and nonprostatic tissues using neural networks. The training of the networks requires extensive sets of pre-processed grayscale images and corresponding manual segmentation maps.…”

Section: A Methods Based On Pixel-by-pixel Classificationmentioning

confidence: 99%

Segmentation of the prostate from suprapubic ultrasound images

Molloy

Acton

2004

Medical Physics

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We present a technique for semiautomated segmentation of human prostates using suprapubic ultrasound (US) images. In this approach, a speckle reducing anisotropic diffusion (SRAD) is applied to enhance the images and the instantaneous coefficient of variation (ICOV) is utilized for edge detection. Segmentation is accomplished via a parametric active contour model in a polar coordinate system that is tailored to the application. The algorithm initially approximates the prostate boundary in two stages. First a primary contour is detected using an elliptical model, followed by a primary contour optimization using an area-weighted mean-difference binary flow geometric snake model. The algorithm was assessed by comparing the computer-derived contours with contours produced manually by three sonographers. The proposed method has application in radiation therapy planning and delivery, as well as in automated volume measurements for ultrasonic diagnosis. The average root mean square discrepancy between computed and manual outlines is less than the inter-observer variability. Furthermore, 76% of the computer-outlined contour is less than 1 sigma manual outline variance away from "true" boundary of prostate. We conclude that the methods developed herein possess acceptable agreement with manually contoured prostate boundaries and that they are potentially valuable tools for radiotherapy treatment planning and verification.

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Section: A Methods Based On Pixel-by-pixel Classificationmentioning

confidence: 99%

Segmentation of the prostate from suprapubic ultrasound images

Molloy

Acton

2004

Medical Physics

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“…Prater also used neural network to segment prostate [7]. These models are criticised for ignoring the shape modelling ultrasound physics [8].…”

Section: Introductionmentioning

confidence: 99%

Using Wavelet and Bayesian Decision Theory in Real-Time Prostate Volume Measurements

Nia

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-The volume of prostate is one of the key indicators in the diagnosis and treatment of prostate cancer. This paper presents a novel method to calculate the volume of prostate in MRI images with high accuracy and in real time. In this approach, wavelet transform is used to decompose a MRI image into spatially oriented channels and then decompose each sub-image into 1D signal, by obtaining integral of subimages. Bayesian decision theory is then used to analyze signals and detect the boundaries of prostate. Experimental results show that the proposed method can be implemented in real time and has acceptable accuracy.

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“…Over the past decade, investigators have explored the use of different algorithms to segment the prostate from 2-D ultrasound images, such as neural networks, 6 texture analysis, 7 and edge detection and linking. [8][9][10][11] In the past five years, deformable models have found success in segmenting a wide range of organs in images obtained from CT, MRI, and US.…”

Section: Introductionmentioning

confidence: 99%

Semiautomatic three-dimensional segmentation of the prostate using two-dimensional ultrasound images

et al. 2003

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In this paper, we report on two methods for semiautomatic three-dimensional (3-D) prostate boundary segmentation using 2-D ultrasound images. For each method, a 3-D ultrasound prostate image was sliced into the series of contiguous 2-D images, either in a parallel manner, with a uniform slice spacing of 1 mm, or in a rotational manner, about an axis approximately through the center of the prostate, with a uniform angular spacing of 5 degrees. The segmentation process was initiated by manually placing four points on the boundary of a selected slice, from which an initial prostate boundary was determined. This initial boundary was refined using the Discrete Dynamic Contour until it fit the actual prostate boundary. The remaining slices were then segmented by iteratively propagating this result to an adjacent slice and repeating the refinement, pausing the process when necessary to manually edit the boundary. The two methods were tested with six 3-D prostate images. The results showed that the parallel and rotational methods had mean editing rates of 20% and 14%, and mean (mean absolute) volume errors of -5.4% (6.5%) and -1.7% (3.1%), respectively. Based on these results, as well as the relative difficulty in editing, we conclude that the rotational segmentation method is superior.

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Segmenting ultrasound images of the prostate using neural networks

Cited by 35 publications

References 9 publications

Segmentation of the prostate from suprapubic ultrasound images

Segmentation of the prostate from suprapubic ultrasound images

Using Wavelet and Bayesian Decision Theory in Real-Time Prostate Volume Measurements

Semiautomatic three-dimensional segmentation of the prostate using two-dimensional ultrasound images

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