Difference between revisions of "Project Week 25/NeedleSegmentation"
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* Refine/clear the segmentations coming from the CNN algorithm. | * Refine/clear the segmentations coming from the CNN algorithm. | ||
− | * Figure out how to transfer MRIs to a server hosting the CNN code and | + | * Figure out how to transfer MRIs to a server hosting the CNN code and get back the results. |
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− | * Clustering and morphological | + | * Clustering and morphological filters for data cleaning. |
− | * Talk with someone | + | * Talk with someone from the core team to figure out how to remotely process the data. |
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<!-- Progress and Next steps (fill out at the end of project week) --> | <!-- Progress and Next steps (fill out at the end of project week) --> |
Revision as of 15:09, 22 June 2017
Home < Project Week 25 < NeedleSegmentation
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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 offers tools to segment needles from MRI/CT. It has mostly been tested on MRI from GYN brachytherapy cases. Anyway, this tool requires manual interaction. Now we want to develop a completely automatic strategy to segment the needles. For this purpose, we 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.
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Illustrations