Documentation/4.5/Modules/SegmentationSmoothing

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Introduction and Acknowledgements

Extension: SlicerProstate Acknowledgments: This work supported in part the National Institutes of Health, National Cancer Institute through the following grants: Quantitative MRI of prostate cancer as a biomarker and guide for treatment, Quantitative Imaging Network (U01 CA151261, PI Fennessy) Enabling technologies for MRI-guided prostate interventions (R01 CA111288, PI Tempany) The National Center for Image-Guided Therapy (P41 EB015898, PI Tempany) Quantitative Image Informatics for Cancer Research (QIICR) (U24 CA180918, PIs Kikinis and Fedorov). Authors: Andrey Fedorov (SPL), Andras Lasso (Queen's University) Contact: Andrey Fedorov, <email>fedorov@bwh.harvard.edu</email> License: Slicer License National Center for Image Guided Therapy (NCIGT)   Quantitative Image Informatics for Cancer Research   Surgical Planning Laboratory (SPL)

Module Description

This module can be used to generate a smooth surface segmentation for datasets that were segmented on images with large slice thickness, leading to "staircase" effect. This module was motivated by the need to generate smooth segmentations for prostate MRI images, which typically have high in-plane resolution (0.5mm) relative to the slice thickness (3mm). The module operates by resampling the input to isotropic voxel resolution and applying Gaussian smoothing to the resampled image. Note that the resulting image will have larger size than the input.

Visualization of the segmentation surface before (green) and after (blue) smoothing using this module. Surfaces for this example were generated without using any decimation or smoothing in the ModelMaker module.

Use Cases

• MRI-ultrasound fusion biopsy of the prostate (primary)
• therapy planning
• treatment response assessment

Tutorials

None at this time ... stay tuned!

Panels and their use

• Input label: input segmentation image
• Output label: smoothed segmentation image
• Label to process (optional): the module operates on only one label. In the cases when multiple labels are present in the input, they will be thresholded, and the result will be smoothed. If label to process is specified, thresholding will be applied to process only the specified label.

References

[1] Fedorov A, Khallaghi S, Antonio Sánchez C, Lasso A, Fels S, Tuncali K, Neubauer Sugar E, Kapur T, Zhang C, Wells W, Nguyen PL, Abolmaesumi P, Tempany C. (2015) Open-source image registration for MRI–TRUS fusion-guided prostate interventions. Int J CARS: 1–10. Available: http://link.springer.com/article/10.1007/s11548-015-1180-7.

[2] Fedorov A, Nguyen PL, Tuncali K, Tempany C. (2015). Annotated MRI and ultrasound volume images of the prostate. Zenodo. http://doi.org/10.5281/zenodo.16396

Information for Developers

• Source code: https://github.com/SlicerProstate/SlicerProstate/tree/master/SegmentationSmoothing