3D Prostate Segmentation in Ultrasound Images Based on Tapered and Deformed Ellipsoids

Mahdavi, Sara; Morris, William; Spadinger, Ingrid; Chng, Nick; Göksel, Orçun; Salcudean, Septimiu E.

doi:10.1007/978-3-642-04271-3_116

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…The resulting 2D contours were reversely tapered and warped to match the initial images. We showed [12] that the method is fast, and produces smooth contours that are in agreement with the brachytherapy requirements. However, the need for manual initialization limits its use for real-time applications and makes it user dependent.…”

Section: Discussionsupporting

confidence: 59%

“…The proposed 2D prostate segmentation method using fused elastography and US image information can be extended into 3D by modifying the method that we proposed in [12] which was based on fitting an a priori shape to a set of parallel transverse ultrasound images. The algorithm was initialized by the user manually selecting some initial boundary points on the mid-slice.…”

Section: Discussionmentioning

confidence: 99%

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Automatic Prostate Segmentation Using Fused Ultrasound B-Mode and Elastography Images

Mahdavi

Moradi

Morris

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. In this paper we propose a fully automatic 2D prostate segmentation algorithm using fused ultrasound (US) and elastography images. We show that the addition of information from mechanical tissue properties acquired from elastography to acoustic information from Bmode ultrasound, can improve segmentation results. Gray level edge similarity and edge continuity in both US and elastography images deform an Active Shape Model. Comparison of automatic and manual contours on 107 transverse images of the prostate show a mean absolute error of 2.6 ± 0.9 mm and a running time of 17.9 ± 12.2 s. These results show that the combination of the high contrast elastography images with the more detailed but low contrast US images can lead to very promising results for developing an automatic 3D segmentation algorithm.

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Section: Discussionsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Automatic Prostate Segmentation Using Fused Ultrasound B-Mode and Elastography Images

Mahdavi

Moradi

Morris

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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“…This is due to the tapering of most prostates both in the 2D transverse plane and in the 3D volume along the main axis from the base to the apex. In Mahdavi and Salcudean (2008) we introduce 2D tapering which is later extended to 3D in Mahdavi et al (2009). The complete approach, together with a more extensive evaluation, are provided in this paper.…”

Section: A Review Of Previous Workmentioning

confidence: 99%

Semi-automatic segmentation for prostate interventions

Mahdavi

Chng

Spadinger

et al. 2011

Medical Image Analysis

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In this paper we report and characterize a semi-automatic prostate segmentation method for prostate brachytherapy. Based on anatomical evidence and requirements of the treatment procedure, a warped and tapered ellipsoid was found suitable as the a priori 3D shape of the prostate. By transforming the acquired endorectal transverse images of the prostate into ellipses, the shape fitting problem was cast into a convex problem which can be solved efficiently. The average whole gland error between volumes created from manual and semi-automatic contours from 21 patients was 6.63±0.9%. For use in brachytherapy treatment planning, the resulting contours were modified, if deemed necessary, by radiation oncologists prior to treatment. The average whole gland volume error between the volumes computed from semi-automatic contours and those computed from modified contours, from 40 patients, was 5.82±4.15%. The amount of bias in the physicians’ delineations when given an initial semi-automatic contour was measured by comparing the volume error between 10 prostate volumes computed from manual contours with those of modified contours. This error was found to be 7.25±0.39% for the whole gland. Automatic contouring reduced subjectivity, as evidenced by a decrease in segmentation inter- and intra-observer variability from 4.65% and 5.95% for manual segmentation to 3.04% and 3.48% for semi-automatic segmentation, respectively. We characterized the performance of the method relative to the reference obtained from manual segmentation by using a novel approach that divides the prostate region into nine sectors. We analyzed each sector independently as the requirements for segmentation accuracy depend on which region of the prostate is considered. The measured segmentation time is 14±1 seconds with an additional 32±14 seconds for initialization. By assuming 1–3 minutes for modification of the contours, if necessary, a total segmentation time of less than 4 minutes is required, with no additional time required prior to treatment planning. This compares favorably to the 5–15 minute manual segmentation time required for experienced individuals. The method is currently used at the British Columbia Cancer Agency (BCCA) Vancouver Cancer Centre as part of the standard treatment routine in low dose rate prostate brachytherapy and is found to be a fast, consistent and accurate tool for the delineation of the prostate gland in ultrasound images.

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“…The contour‐based methods use the prostate boundary or edge information to guide the segmentation . As they depend on reliable edge information, they may be adversely affected by the presence of shadow artifacts seen on the ultrasound images .…”

Section: Introductionmentioning

confidence: 99%

“…The contour-based methods use the prostate boundary or edge information to guide the segmentation. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] As they depend on reliable edge information, they may be adversely affected by the presence of shadow artifacts seen on the ultrasound images. 24 The region-based methods use the predominant intensity distributions of the prostate region to guide the segmentation.…”

Section: Introductionmentioning

confidence: 99%

A random walk‐based segmentation framework for 3D ultrasound images of the prostate

Guo

Tian

et al. 2017

Medical Physics

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Purpose Accurate segmentation of the prostate on ultrasound images has many applications in prostate cancer diagnosis and therapy. Transrectal ultrasound (TRUS) has been routinely used to guide prostate biopsy. This manuscript proposes a semiautomatic segmentation method for the prostate on three-dimensional (3D) TRUS images. Methods The proposed segmentation method uses a context-classification based, random walk algorithm. Because context information reflects patient-specific characteristics and prostate changes in the adjacent slices, and classification information reflects population-based prior knowledge, we combine the context and classification information at the same time in order to define the applicable population and patient-specific knowledge so as to more accurately determine the seed points for the random walk algorithm. The method is initialized with the user drawing the prostate and non-prostate circles on the mid-gland slice and then automatically segments the prostate on other slices. To achieve reliable classification, we use a new adaptive k-means algorithm to cluster the training data and train multiple decision tree classifiers. According to the patient-specific characteristics, the most suitable classifier is selected and combined with the context information in order to locate the seed points. By providing accuracy locations of the seed points, the random walk algorithm improves segmentation performance. Results We evaluate the proposed segmentation approach on a set of 3D TRUS volumes of prostate patients. The experimental results show that our method achieved a Dice similarity coefficient of 91.0% ± 1.6% as compared to manual segmentation by clinically experienced radiologist. Conclusions The random walk based segmentation framework, which combines patient-specific characteristics and population information, is effective for segmenting the prostate on ultrasound images. The segmentation method can have various applications in ultrasound-guided prostate procedures.

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3D Prostate Segmentation in Ultrasound Images Based on Tapered and Deformed Ellipsoids

Cited by 11 publications

References 17 publications

Automatic Prostate Segmentation Using Fused Ultrasound B-Mode and Elastography Images

Automatic Prostate Segmentation Using Fused Ultrasound B-Mode and Elastography Images

Semi-automatic segmentation for prostate interventions

A random walk‐based segmentation framework for 3D ultrasound images of the prostate

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