Semiautomatic segmentation for prostate brachytherapy: Dosimetric evaluation

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

doi:10.1016/j.brachy.2011.07.007

Cited by 15 publications

(19 citation statements)

References 23 publications

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“…Twelve sectors were considered: left, right, anterior and posterior in the base, mid-gland and apex [27]. Multi-parametric magnetic resonance imaging findings and TTMB results were manually mapped onto the corresponding 12 sectors.…”

Section: Resultsmentioning

confidence: 99%

Focal application of low-dose-rate brachytherapy for prostate cancer: a pilot study

Mahdavi¹,

Spadinger²,

Salcudean³

et al. 2017

jcb

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PurposeTo evaluate the feasibility and to report the early outcomes of focal treatment of prostate cancer using low-dose-rate brachytherapy (LDR-PB).Material and methodsSeventeen patients were screened with multi-parametric magnetic resonance imaging (mpMRI), 14 of whom proceeded to receive trans-perineal template mapping biopsy (TTMB). Focal LDR-PB was performed on five eligible patients using dual air kerma strength treatment plans based on planning target volumes derived from cancer locations and determined by TTMB. Patient follow-up includes prostate specific antigen (PSA) measurements, urinary and sexual function questionnaires, repeated imaging and TTMB at specific intervals post-treatment.ResultsFeasibility of focal LDR-PB was shown and short-term outcomes are promising. While the detection rate of tumors, a majority of which were low grade GS 3 + 3, was found to be low on mpMRI (sensitivity of 37.5%), our results suggest the potential of mpMRI in detecting the presence of higher grade (GS ≥ 3 + 4), and bilateral disease indicating its usefulness as a screening tool for focal LDR-PB.ConclusionsLow-dose-rate brachytherapy is a favorable ablation option for focal treatment of prostate cancer, requiring minimal modification to the standard (whole gland) LDR-PB treatment, and appears to have a more favorable side effect profile. Further investigation, in the form of a larger study, is needed to assess the methods used and the long-term outcomes of focal LDR-PB.

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Section: Resultsmentioning

confidence: 99%

Focal application of low-dose-rate brachytherapy for prostate cancer: a pilot study

Mahdavi¹,

Spadinger²,

Salcudean³

et al. 2017

jcb

Self Cite

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“…Other representations, including ellipse [34][35][36][37], catenary curve [14], super ellipse [38], tapered super ellipse [39], superquadrics [40], and spherical harmonics [41], have been used and tested. They primarily involve parametric curves.…”

Section: Shapementioning

confidence: 99%

“…Brachytherapy planning is based on the use of TRUS images that are acquired in the treatment room [37]. The images are used to define the prostate contours and the positions of the seeds or the catheters.…”

Section: Brachytherapymentioning

confidence: 99%

A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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Computer technology is widely used for multimodal image analysis of the prostate gland. Several techniques have been developed, most of which incorporate a priori knowledge extracted from organ features. Knowledge extraction and modeling are multi-step tasks. Here, we review these steps and classify the modeling according to the data analysis methods employed and the features used. We conclude with a survey of some clinical applications where these techniques are employed.

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“…However, interpolation methods are still time consuming and they have high computational cost as the authors documented. Mahdavi et al [38] proposed a semi-automatic segmentation for prostate interventions. In this method manual initialization of the shape priors makes use of the physicians experience.…”

Section: B Related Workmentioning

confidence: 99%

“…Refer to [1,2,5,7,9,[12][13][14][15][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] for publication of the shape based segmentation methods.…”

Section: Minimizing -Log P(l Lflmentioning

confidence: 99%

Probabilistic and geometric shape based segmentation methods.

Aslan¹

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Aslan, Melih Seref, "Probabilistic and geometric shape based segmentation methods." (2012 Image segmentation is one of the most important problems in image processing, object recognition, computer vision, medical imaging, etc. In general, the objective of the segmentation is to partition the image into the meaningful areas using the existing (low level) information in the image and prior (high level) information which can be obtained using a number of features of an object. As stated in [1,2], the human vision system aims to extract and use as much information as possible in the image including but not limited to the intensity, possible motion of the object (in sequential images), spatial relations (interaction) as the existing information, and the shape of the object which is learnt from the experience as the prior information. The main objective of this dissertation is to couple the prior information with the existing information since the machine vision system cannot predict the prior information unless it is given.To label the image into meaningful areas, the chosen information is modelled to fit progressively in each of the regions by an optimization process. The intensity and spatial interaction (as the existing information) and shape (as the prior information) are modelled to obtain the optimum segmentation in this study.The intensity information is modelled using the Gaussian distribution. Spatial inv teraction that describes the relation between neighboring pixels/voxels is modeled by assuming that the pixel intensity depends on the intensities of the neighboring pixels. The shape model is obtained using occurrences of histogram of training shape pixels or voxels. The main objective is to capture the shape variation of the object of interest. Each pixel in the image will have three probabilities to be an object and a background class based on the intensity, spatial interaction, and shape models. These probabilistic values will guide the energy (cost) functionals in the optimization process.This dissertation proposes segmentation frameworks which has the following properties: i) original to solve some of the existing problems, ii) robust under various segmentation challenges, and iii) fast enough to be used in the real applications. In this dissertation, the models are integrated into different methods to obtain the optimum segmentation: 1) variational (can be considered as the spatially continuous), and 2) statistical (can be considered as the spatially discrete) methods.The proposed segmentation frameworks start with obtaining the initial segmentation using the intensity / spatial interaction models. The shape model, which is obtained using the training shapes, is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of the energy functionals. Experiments show that the use of the shape prior improves considerably the accuracy of the alternative methods which use only existing or both information in the image. The proposed methods are tested on the syntheti...

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Semiautomatic segmentation for prostate brachytherapy: Dosimetric evaluation

Cited by 15 publications

References 23 publications

Focal application of low-dose-rate brachytherapy for prostate cancer: a pilot study

Focal application of low-dose-rate brachytherapy for prostate cancer: a pilot study

A survey of prostate modeling for image analysis

Probabilistic and geometric shape based segmentation methods.

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