NA-MIC Project Weeks

3D Printed Prostate Cancer Models

## 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)

Project Description

The goal of this project is to create a workflow to create pre-operative 3D prostate cancer models from multi-parametric MRI data.  

Objective

1. Segment the prostate, dominant lesion, neurovascular bundles, bladder neck, urethra,and rectal wall.

2. Prepare segmented imaging data for 3D printing.  

   Approach and Plan

3. Learn how to use relevant 3D Slicer modules for prostate segmentation (modules/extensions to consider: Segment Editor, DeepInfer, TOMAAT, SlicerProstate).

4. Review methods to segment other pertinent prostate anatomy in Slicer.

5. Create optimal outputs for 3D printing.

Progress and Next Steps

1. Learned and tested 3D Slicer segmentation tools. Compared surface cut tool to manual segmentation and grow from seeds.

2. Continue to work on workflow for segmentation and printing- discuss with Danielle Pace.

3. Compare 3D Slicer models to models created with other software platforms.

4. Work with Anneke Meyer on deep learning for prostate segmentation for 3D Printed models.

Background and References

• 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