3D automatic segmentation and reconstruction of prostate on MR images

Betrouni, Nacim; Puech, Philippe; Dewalle, A.S.; Lopes, Renaud; Dubois, Patrick; Vermandel, Maximilien

doi:10.1109/iembs.2007.4353528

Cited by 12 publications

(3 citation statements)

References 15 publications

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“…In a previous work [17,18], a generic prostate model had been established from a training base of 20 manual outlines. The statistical shape model [16,19], deduced by a principal component analysis (PCA), is composed of an average shape and the most important deformation directions.…”

Section: D Modelmentioning

confidence: 99%

Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI

et al. 2008

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Section: D Modelmentioning

confidence: 99%

Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI

et al. 2008

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“…As some software will automatically segment the prostate on T2WI, this further supports its use for target segmentation. [17][18][19] …”

Section: Optimized Mpmri Protocol and Reporting For Image Fusion Targmentioning

confidence: 99%

MR-TRUS Fusion Biopsy

Margolis

2016

Topics in Magnetic Resonance Imaging

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The leading application of multiparametric magnetic resonance imaging (mpMRI) of the prostate is for lesion detection with the intention of tissue sampling (biopsy). Although direct in-bore magnetic resonance (MR)-guided biopsy allows for confirmation of the biopsy site, this can be expensive, time-consuming, and most importantly limited in availability. MR-transrectal ultrasound (MR-TRUS) image fusion targeted biopsy (TBx) allows for lesions identified on MRI to be targeted with the ease, efficiency, and availability of ultrasound.The learning objectives are optimized mpMRI protocol and reporting for image fusion targeted biopsy; methods of TRUS TBx; performance and limitations of MR-TRUS TBx; future improvements and applications.

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“…Betrouni et al proposed a 3D deformable model [3], which utilizes Iterative Closest Point (ICP) algorithm and Principle Component Analysis (PCA) to deform 3D prostate contour points and to reach the final segmentation result. A fuzzy set algorithm is used to reconstruct the 3D prostate surface based on slice profile.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and surface reconstruction model of prostate MRI to improve prostate cancer diagnosis

Cheng

Bernardo

Senseney

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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This work presents a MRI prostate segmentation and surface reconstruction model that facilities the validation of multiparametric MRI with histopathology slides from radical prostatectomy specimens and targeted biopsy specimens. Application of this technique combines image processing and computer aided design to provide a generalized solution to construct a high resolution 3D prostate surface from MRI images in three orthogonal views with non-isotropic voxel resolution.The performance of the segmentation is evaluated with 45 MR image datasets, yielding an average segmentation accuracy of 90%.

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3D automatic segmentation and reconstruction of prostate on MR images

Abstract: In this work we present a method to automatic 3D segmentation of prostate on MR images and volume reconstruction by fuzzy sets fusion algorithm. The segmentation is model based method and the reconstruction takes into account the slice thickness to reduce the partial volume effect.

Cited by 12 publications

References 15 publications

Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI

Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI

MR-TRUS Fusion Biopsy

Segmentation and surface reconstruction model of prostate MRI to improve prostate cancer diagnosis

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