Difference between revisions of "Project Week 25/NeedleSegmentation"
From NAMIC Wiki
Line 30: | Line 30: | ||
| | | | ||
<!-- Progress and Next steps (fill out at the end of project week) --> | <!-- Progress and Next steps (fill out at the end of project week) --> | ||
− | + | * A bridge between data coming by the unet and the already existing NeedleFinder module was implemented. Now we are able to use the segmentations generated by the unet to initialize the NeedleFinder algorithm. The process is completely automatic. | |
+ | * We had an interesting discussion with Steve about DeepInfer. | ||
|} | |} | ||
Revision as of 08:48, 30 June 2017
Home < Project Week 25 < NeedleSegmentation
Back to Projects List
Key Investigators
- Paolo Zaffino (Magna Graecia University, Italy)
- Salvatore Scaramuzzino (Magna Graecia University/ASL Vercelli, Italy)
- Maria Francesca Spadea (Magna Graecia University, Italy)
- Guillaume Pernelle (remote) (Imperial College, London, UK)
- Alireza Mehrtash (remote) (Brigham and Women's Hospital, Harvard Medical School, USA)
- Tina Kapur (Brigham and Women's Hospital, Harvard Medical School, USA)
Project Description
NeedleFinder is a tool for segmentation of needles from MR scans which requires manual initialization of the tip of the needle. It has been tested extensively on MR-guided gynecologic brachytherapy data, and preliminarily on MR-guided prostate biopsy data. In this project, we aim to eliminate this reliance on manual interaction and develop a completely automatic strategy to segment the needles. We have tested a CNN approach that provides good results, even if a post processing step must be implemented in order to remove some noise and to refine the obtained segmentations.
Objective | Approach and Plan | Progress and Next Steps |
---|---|---|
|
|
|
Illustrations