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Prostate segmentation for medical interventions Mahdavi, Seyedeh Sara

Abstract

Prostate cancer is the most prevalent type of cancer among men. Accurate delineation and appropriate visualization of the prostatic region can greatly affect treatment of prostate cancer and has the potential to reduce some of the treatment side-effects. The main goal of this research is to develop a prostate segmentation tool which is suitable to replace manual delineation. Manual segmentation, the current standard in procedures such as low dose rate prostate brachytherapy, is tedious, time consuming and observer dependent. We propose a 3D semi-automatic segmentation tool to overcome these limitations. To show the clinical value of this method we perform extensive dosimetric evaluation on in-vivo ultrasound images. This tool is currently being clinically used as part of the prostate brachytherapy treatment procedure at the BC Cancer Agency. Ultrasound is the common modality for imaging the prostate. Although safe and simple to use, it can not always allow the prostate to be reliably delineated. Vibro-elastography is a relatively new imaging method which is used to characterize mechanical properties of tissue. We investigate the suitability of vibro-elastography for visualizing the prostate. We compare in-vivo B-mode ultrasound and vibro-elastograpy images with the gold standard MRI in terms of contrast, edge visibility and the shape and size of the gland as seen in these images. Based on our results we develop a 3D automatic prostate segmentation tool in which, in addition to B-mode, information from vibro-elastography images is used in an iterative model-based segmentation approach. We conclude this work by studying the visibility of cancer itself in vibro-elastography images. Areas suspected for cancer are manually marked on the images and the results are compared to the marked cancer in registered pathology slices. Our preliminary results show that vibro-elastography has the potential to be used for detecting prostate cancer; however, we suggest a combined use of various modalities or image types to improve cancer detection.

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Attribution-NonCommercial-NoDerivatives 4.0 International

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