Modeling prostate anatomy from multiple view TRUS images for image-guided HIFU therapy

Penna, Michael A.; Dines, K.A.; Seip, R.; Carlson, Roy; Sanghvi, N. T.

doi:10.1109/tuffc.2007.211

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…While the extensive study of [12] showed good segmentation results, the method presented is limited to 2D. Previous 3D methods [15,16] are time consuming (> 2 minutes) or require significant user intervention [11]. Our use of an a priori shape allows prostate segmentation in poorly visible regions and therefore can be used in standard clinical practice, thus enabling a more complete validation study than presented with other 3D methods.…”

Section: Introductionmentioning

confidence: 99%

“…Deformable models have been widely used for medical image segmentation [5,6,7,8,9] and are generally more successful than the former methods. Fitting ellipses, ellipsoids, super-ellipses, and deformable ellipses or using them for initialization have been relatively attractive approaches for prostate segmentation due to the shape of the gland [10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

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

Mahdavi

Morris

Spadinger

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. Prostate segmentation from trans-rectal transverse B-mode ultrasound images is required for radiation treatment of prostate cancer. Manual segmentation is a time-consuming task, the results of which are dependent on image quality and physicians' experience. This paper introduces a semi-automatic 3D method based on super-ellipsoidal shapes. It produces a 3D segmentation in less than 15 seconds using a warped, tapered ellipsoid fit to the prostate. A study of patient images shows good performance and repeatability. This method is currently in clinical use at the Vancouver Cancer Center where it has become the standard segmentation procedure for low dose-rate brachytherapy treatment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mahdavi

Morris

Spadinger

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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show abstract

“…With proper choice of operating frequency and transducer design, this can be achieved without significant biological damage to the intervening tissue [9,10]. The advent of image guidance modalities such as MRI and ultrasound has allowed for completely non-invasive thermal therapy procedures [11][12][13][14]. Different applications of image-guided HIFU therapy for potential medical applications have been proposed earlier [15,16] in addition to a computational model for ultrasound tomography [17].…”

Section: Introductionmentioning

confidence: 99%

Anatomical-based model for simulation of HIFU-induced lesions in atherosclerotic plaques

Almekkaway

Shehata

Ebbini

2015

International Journal of Hyperthermia

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Purpose: The aim of this study was to simulate the effect of high intensity focused ultrasound (HIFU) in non-homogenous medium for targeting atherosclerotic plaques in vivo. Materials and methods: A finite-difference time-domain heterogeneous model for acoustic and thermal tissue response in the treatment region was derived from ultrasound images of the treatment region. A 3.5 MHz dual mode ultrasound array suitable for targeting peripheral vessels was used. The array has a lateral and elevation focus at 40 mm with fenestration in its centre through which a 7.5 MHz diagnostic transducer can be placed. Two cases were simulated where seven adjacent HIFU shots ($5000 W/cm 2 , 2-s exposure time) were targeted on the plaque tissue within the femoral artery. The transient bioheat equation with a convective term to account for blood flow was used to predict the thermal dose. The results of the simulation model were then validated against the histology data. Results: The simulation model predicted the HIFU-induced damage for both cases, and correlated well with the histology data. For the first case thermal damage was detected within the targeted plaque, while for the second case thermal damage was detected in the pre-focal region. Conclusion: The results suggest that a realistic, image-based acoustic and thermal model of the treatment region is capable of predicting the extent of thermal damage to target plaque tissue. The model considered the effect of the wall thickness of large arteries and the heat-sink effect of flowing blood. The model is used for predicting the size and pattern of HIFU damage in vivo.

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“…Manual segmentation is time consuming and, due to the low signal to noise ratio of ultrasound images, inter and intraobserver variabilities are high. Even though various 2D prostate segmentation methods and some 3D methods have been proposed in the literature [1,2,3,4,5], the effective automatic segmentation of ultrasound images of the prostate has remaining challenges such as user initialization and limited accuracy.…”

Section: Introductionmentioning

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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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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Modeling prostate anatomy from multiple view TRUS images for image-guided HIFU therapy

Cited by 12 publications

References 22 publications

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

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

Anatomical-based model for simulation of HIFU-induced lesions in atherosclerotic plaques

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

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