Difference between revisions of "Prostate segmentation and surface reconstruction"
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We present the instructions for a MRI prostate segmentation and surface reconstruction model that facilities the validation of multi-parametric MRI with histopathology slides from radical prostatectomy specimens and targeted biopsy specimens. Application of this technique combines image processing and computer aided design to construct a high resolution 3D prostate surface from MRI images in three orthogonal views with non-isotropic voxel resolution. The segmentation tool is from MIPAV software, which is freely available and open source. | We present the instructions for a MRI prostate segmentation and surface reconstruction model that facilities the validation of multi-parametric MRI with histopathology slides from radical prostatectomy specimens and targeted biopsy specimens. Application of this technique combines image processing and computer aided design to construct a high resolution 3D prostate surface from MRI images in three orthogonal views with non-isotropic voxel resolution. The segmentation tool is from MIPAV software, which is freely available and open source. | ||
| − | The proposed model has three major building blocks: 1) Semi-automatic B-Spline registration-guided segmentation model, 2) Ball-pivoting and Poisson based 3D surface reconstruction, and 3) 3D printing for prostate mold. The proposed model is shown in Figure 1. | + | The proposed model has three major building blocks: 1) Semi-automatic B-Spline registration-guided segmentation model, 2) Ball-pivoting and Poisson based 3D surface reconstruction, and 3) 3D printing for prostate mold. The proposed model is shown in Figure 1 (TBD). |
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| + | We present instructions on using the segmentation tool in step-wise order. An image dataset is also provided with the instruction, and the dataset can be obtained from: . For the image acquisition, the input data are MR images, which are obtained from a 3.0 T whole-body MRI system (Achieva, Philips Healthcare). T2-weighted MR images of the entire prostate were obtained in three orthogonal planes (sagittal, axial and coronal) at the scan resolution of 0.2734x0.2734x3.0 mm3; field of view 140 mm; image slice dimension 512x512. The center of the prostate is the focal point for the MRI scan. To reduce the scan time, a lower refocusing pulse of 100 degrees was used for sagittal and coronal images, which alters the contrast on these images compared to the axial images. | ||
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Revision as of 22:00, 19 February 2013
We present the instructions for a MRI prostate segmentation and surface reconstruction model that facilities the validation of multi-parametric MRI with histopathology slides from radical prostatectomy specimens and targeted biopsy specimens. Application of this technique combines image processing and computer aided design to construct a high resolution 3D prostate surface from MRI images in three orthogonal views with non-isotropic voxel resolution. The segmentation tool is from MIPAV software, which is freely available and open source.
The proposed model has three major building blocks: 1) Semi-automatic B-Spline registration-guided segmentation model, 2) Ball-pivoting and Poisson based 3D surface reconstruction, and 3) 3D printing for prostate mold. The proposed model is shown in Figure 1 (TBD).
We present instructions on using the segmentation tool in step-wise order. An image dataset is also provided with the instruction, and the dataset can be obtained from: . For the image acquisition, the input data are MR images, which are obtained from a 3.0 T whole-body MRI system (Achieva, Philips Healthcare). T2-weighted MR images of the entire prostate were obtained in three orthogonal planes (sagittal, axial and coronal) at the scan resolution of 0.2734x0.2734x3.0 mm3; field of view 140 mm; image slice dimension 512x512. The center of the prostate is the focal point for the MRI scan. To reduce the scan time, a lower refocusing pulse of 100 degrees was used for sagittal and coronal images, which alters the contrast on these images compared to the axial images.
