Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry

Yuan, Jing; Qiu, Wu; Rajchl, Martin; Ukwatta, Eranga; Tai, Xue–Cheng; Fenster, Aaron

doi:10.1109/cvpr.2013.287

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…One study, published by Ukwatta et al [45], described the segmentation of the carotid artery in 3D MRI images. The same research group also used this technique to extract myocardial scar tissue [38], to segment the femoral artery lumen and outer wall surfaces [46], to segment lateral ventricles in preterm neonates with intraventricular hemorrhage [36], and to delineate 3D prostate boundaries for the planning and guiding of prostate biopsies [37,53].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

Suárez-Mejías

Pérez-Carrasco

Serrano

et al. 2016

Med Biol Eng Comput

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An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuous convex relaxation methodology, the main novelty being the introduction of accumulated gradient distance, with intensity and gradient information being incorporated into the segmentation process. The algorithm was used to segment 26 CT image volumes. The results were compared with manual contouring of the same tumors. The proposed algorithm achieved 90 % sensitivity, 100 % specificity and 84 % positive predictive value, obtaining a mean distance to the closest point of 3.20 pixels. The algorithm's dependence on the initial manual contour was also analyzed, with results showing that the algorithm substantially reduced the variability of the manual segmentation carried out by different specialists. The algorithm was also compared with four benchmark algorithms (thresholding, edge-based level-set, region-based level-set and continuous max-flow with two labels). To the best of our knowledge, this is the first time the segmentation of retroperitoneal tumors for radiotherapy planning has been addressed.

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Section: Introductionmentioning

confidence: 99%

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

Suárez-Mejías

Pérez-Carrasco

Serrano

et al. 2016

Med Biol Eng Comput

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“…In contrast, the proposed approach introduced in this paper avoids these disadvantages by jointly optimizing the segmentations of all the slices under a "global" perspective. A preliminary study of this work has been previously described in CVPR conference [33], which was only validated on a small number of 3-D TRUS images. More details on the algorithm and its implementation are provided in this paper.…”

mentioning

confidence: 99%

Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images

Qiu

Yuan

Ukwatta

et al. 2014

IEEE Trans. Med. Imaging

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We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a "global" 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2%±2.0% for 3-D TRUS images and 88.5%±3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers.

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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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Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry

Abstract: Segmenting 3D endfiring transrectal ultrasound (TRUS) prostate images efficiently and accurately is of utmost im-

Cited by 10 publications

References 21 publications

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning

Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images

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

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