Key Investigators
- Nicole Wake (NYU School of Medicine)
- Hersh Chandarana (NYU School of Medicine)
- Andrew Rosenkrantz (NYU School of Medicine)
- William Huang (NYU School of Medicine)
- Andrey Fedorov (BWH, HMS)
- Danielle Pace (MIT) (has put together a pipeline for preparing 3d printed models before, happy to share experience)
- Anneke Meyer (University of Magdeburg, Germany)
The goal of this project is to create a workflow to create pre-operative 3D prostate cancer models from multi-parametric MRI data.
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Segment the prostate, dominant lesion, neurovascular bundles, bladder neck, urethra,and rectal wall.
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Prepare segmented imaging data for 3D printing.
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Learn how to use relevant 3D Slicer modules for prostate segmentation (modules/extensions to consider: Segment Editor, DeepInfer, TOMAAT, SlicerProstate).
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Review methods to segment other pertinent prostate anatomy in Slicer.
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Create optimal outputs for 3D printing.
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Learned and tested 3D Slicer segmentation tools. Compared surface cut tool to manual segmentation and grow from seeds.
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Continue to work on workflow for segmentation and printing- discuss with Danielle Pace.
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Compare 3D Slicer models to models created with other software platforms.
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Work with Anneke Meyer on deep learning for prostate segmentation for 3D Printed models.
- Wake N, Chandarana H, Huang WC, Taneja SS, Rosenkrantz AB. Application of anatomically accurate, patient-specific 3D printed models from MRI data in urological oncology. Clin Radiol. 2016;71(6):610-4. http://dx.doi.org/10.1016/j.crad.2016.02.012. http://www.clinicalradiologyonline.net/article/S0009-9260(16)00087-8/fulltext
- Wake N, Rude T, Kang SK, et al. 3D printed renal cancer models derived from MRI data: application in pre-surgical planning. Abdom Radiol (NY). 2017;42(5):1501-9. http://dx.doi.org/10.1007/s00261-016-1022-2. https://link.springer.com/article/10.1007/s00261-016-1022-2