Automatic Segmentation of Pelvic Structures From Magnetic Resonance Images for Prostate Cancer Radiotherapy

Pasquier, David; Lacornerie, T.; Vermandel, Maximilien; Rousseau, Jean; Lartigau, Éric; Betrouni, Nacim

doi:10.1016/j.ijrobp.2007.02.005

Cited by 111 publications

(80 citation statements)

References 45 publications

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“…Automatic segmentation algorithms overcome the limitations imposed by the complexity of shapes and images through the use of shape and appearance models that serve to automatically detect the margin of the prostate (30). Automated procedures relieve radiologists of time-consuming manual segmentation, while maintaining high levels of accuracy and reproducibility, equal to or even greater than those of the manual method (31).…”

Section: Semiautomated Segmentationmentioning

confidence: 99%

Clinical value of prostate segmentation and volume determination on MRI in benign prostatic hyperplasia

Garvey¹,

Türkbey²,

Truong³

et al. 2014

Diagn Interv Radiol

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Benign prostatic hyperplasia (BPH) is a nonmalignant pathological enlargement of the prostate, which occurs primarily in the transitional zone. BPH is highly prevalent and is a major cause of lower urinary tract symptoms in aging males, although there is no direct relationship between prostate volume and symptom severity. The progression of BPH can be quantified by measuring the volumes of the whole prostate and its zones, based on image segmentation on magnetic resonance imaging. Prostate volume determination via segmentation is a useful measure for patients undergoing therapy for BPH. However, prostate segmentation is not widely used due to the excessive time required for even experts to manually map the margins of the prostate. Here, we review and compare new methods of prostate volume segmentation using both manual and automated methods, including the ellipsoid formula, manual planimetry, and semiautomated and fully automated segmentation approaches. We highlight the utility of prostate segmentation in the clinical context of assessing BPH. Benign prostatic hyperplasia (BPH) can result in lower urinary tract symptoms, and is one of the most common diseases affecting aging men. BPH can compromise quality of life and is a major healthcare cost. Despite the high prevalence of BPH, few methods of accurately assessing prostate volume are actually used in clinical practice. While patient assessment of urinary symptoms dictates the need for treatment, it is highly subjective, whereas prostate volume change is a more objective measure of treatment response. The most common clinical model for approximating the prostate gland size is the ellipsoid model from transrectal ultrasonography (TRUS) imaging, which has been shown to underestimate prostate volume for prostates larger than 50 mL and to overestimate prostate volume for glands smaller than 30 mL (1). Despite its limitations, the TRUS method of prostate volume assessment is preferred in current clinical practice due to its availability and cost and time efficiency (2). More accurate prostate volume measurement with magnetic resonance (MR) planimetry is time-intensive and, thus, rarely performed.Prostate segmentation is an accurate technique for prostate volume determination that can be used in coregistration with various imaging modalities, such as magnetic resonance imaging (MRI) with positron emission tomography and MRI with ultrasonography. Segmentation can be used for both diagnostic and interventional procedures, including guided biopsies and focal ablation. Newly developed methods of automated prostate segmentation allow for efficient prostate volume determination, thereby enhancing decision support systems and computer-aided diagnosis tools.This article reviews the major methods of prostate volume determination currently in use, including the ellipsoid formula, manual planimetry, and semiautomated and fully automated segmentation. A clinical overview of BPH is also provided to highlight the utility of prostate segmentation in the clinical management...

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Section: Semiautomated Segmentationmentioning

confidence: 99%

Clinical value of prostate segmentation and volume determination on MRI in benign prostatic hyperplasia

Garvey¹,

Türkbey²,

Truong³

et al. 2014

Diagn Interv Radiol

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“…Deformable-model-based segmentation methods, which combine statistical information about organ shape and image appearance, have been proven effective for segmenting the prostate from CT, 1-5 MRI, [6][7][8] or ultrasound images. [9][10][11][12][13] An extensive review of statistical-shape-model-based medical object segmentation can be found in Ref.…”

Section: Introductionmentioning

confidence: 99%

Segmenting CT prostate images using population and patient‐specific statistics for radiotherapy

Feng¹,

et al. 2010

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Purpose: In the segmentation of sequential treatment-time CT prostate images acquired in imageguided radiotherapy, accurately capturing the intrapatient variation of the patient under therapy is more important than capturing interpatient variation. However, using the traditional deformablemodel-based segmentation methods, it is difficult to capture intrapatient variation when the number of samples from the same patient is limited. This article presents a new deformable model, designed specifically for segmenting sequential CT images of the prostate, which leverages both population and patient-specific statistics to accurately capture the intrapatient variation of the patient under therapy. Methods: The novelty of the proposed method is twofold: First, a weighted combination of gradient and probability distribution function ͑PDF͒ features is used to build the appearance model to guide model deformation. The strengths of each feature type are emphasized by dynamically adjusting the weight between the profile-based gradient features and the local-region-based PDF features during the optimization process. An additional novel aspect of the gradient-based features is that, to alleviate the effect of feature inconsistency in the regions of gas and bone adjacent to the prostate, the optimal profile length at each landmark is calculated by statistically investigating the intensity profile in the training set. The resulting gradient-PDF combined feature produces more accurate and robust segmentations than general gradient features. Second, an online learning mechanism is used to build shape and appearance statistics for accurately capturing intrapatient variation. Results: The performance of the proposed method was evaluated on 306 images of the 24 patients. Compared to traditional gradient features, the proposed gradient-PDF combination features brought 5.2% increment in the success ratio of segmentation ͑from 94.1% to 99.3%͒. To evaluate the effectiveness of online learning mechanism, the authors carried out a comparison between partial online update strategy and full online update strategy. Using the full online update strategy, the mean DSC was improved from 86.6% to 89.3% with 2.8% gain. On the basis of full online update strategy, the manual modification before online update strategy was introduced and tested, the best performance was obtained; here, the mean DSC and the mean ASD achieved 92.4% and 1.47 mm, respectively. Conclusions: The proposed prostate segmentation method provided accurate and robust segmentation results for CT images even under the situation where the samples of patient under radiotherapy were limited. A conclusion that the proposed method is suitable for clinical application can be drawn.

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“…Tsai et al use a region-based level set with shape prior to 3D segmentation of prostate MRI [5]. Snakes and region-growing are utilized in another work for prostate, bladder, and rectum segmentation [6]. Zhang et al propose an interactive method utilizing edgebased level set [7].…”

Section: Prostate Segmentationmentioning

confidence: 99%

Multi-Resolution Level Sets with Shape Priors: A Validation Report for 2D Segmentation of Prostate Gland in T2W MR Images

2014

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The level set approach to segmentation of medical images has received considerable attention in recent years. Evolving an initial contour to converge to anatomical boundaries of an organ or tumor is a very appealing method, especially when it is based on a well-defined mathematical foundation. However, one drawback of such evolving method is its high computation time. It is desirable to design and implement algorithms that are not only accurate and robust but also fast in execution. Bresson et al. have proposed a variational model using both boundary and region information as well as shape priors. The latter can be a significant factor in medical image analysis. In this work, we combine the variational model of level set with a multi-resolution approach to accelerate the processing. The question is whether a multi-resolution context can make the segmentation faster without affecting the accuracy. As well, we investigate the question whether a premature convergence, which happens in a much shorter time, would reduce accuracy. We examine multiple semiautomated configurations to segment the prostate gland in T2W MR images. Comprehensive experimentation is conducted using a data set of a 100 patients (1,235 images) to verify the effectiveness of the multi-resolution level set with shape priors. The results show that the convergence speed can be increased by a factor of ≈ 2.5 without affecting the segmentation accuracy. Furthermore, a premature convergence approach drastically increases the segmentation speed by a factor of ≈17.9.

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Automatic Segmentation of Pelvic Structures From Magnetic Resonance Images for Prostate Cancer Radiotherapy

Cited by 111 publications

References 45 publications

Clinical value of prostate segmentation and volume determination on MRI in benign prostatic hyperplasia

Clinical value of prostate segmentation and volume determination on MRI in benign prostatic hyperplasia

Segmenting CT prostate images using population and patient‐specific statistics for radiotherapy

Multi-Resolution Level Sets with Shape Priors: A Validation Report for 2D Segmentation of Prostate Gland in T2W MR Images

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