Difference between revisions of "2017 Winter Project Week/ProstateSectorSegmentation"

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* Test with ProstateX challenge dataset (where zones of findings are given) to predict the zone with Deep Learning
 
* Test with ProstateX challenge dataset (where zones of findings are given) to predict the zone with Deep Learning
 
* if more training data is available: deep learning for an automatic sector segmentation
 
* if more training data is available: deep learning for an automatic sector segmentation
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* (Try (model-based) segmentation approach (costs for segmentation optimization can be derived for example from supervised classification of the gland tissue). The shape of individual sector models could be used as segmentation prior)
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* Try atlas-based segmentation approach to map zone segmentations on more datasets
  
  
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<!-- Progress and Next steps bullet points (fill out at the end of project week) -->
 
<!-- Progress and Next steps bullet points (fill out at the end of project week) -->
 
* Creation of 3 zone segmentations of the prostate (different volumes)
 
* Creation of 3 zone segmentations of the prostate (different volumes)
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* Prediction of zones in ProstateX challenge with Deep Learning is in progress
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* Prediction of zones for specific locations in ProstateX dataset with Deep Learning is in progress
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* Next Steps: Create 3D model with higher resolution. Atlas based segmentation on more publicly available data.
 
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==Background and References==
 
==Background and References==
 
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data -->
 
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data -->

Latest revision as of 15:52, 13 January 2017

Home < 2017 Winter Project Week < ProstateSectorSegmentation

  • Transversal slice of Prostate MRI.

  • Sector Map according to PI-RADS v2.

  • Manual Segmentation on transversal slices.

Key Investigators

  • Anneke Meyer, University of Magdeburg (Germany)
  • Andrey Fedorov, BWH
  • Alireza Mehrtash, BWH
  • Christian Hansen, University of Magdeburg (Germany)
  • Teodora Szasz, University of Chicago

Project Description

Objective Approach and Plan Progress and Next Steps
  • Segmentation of prostate and its sectors
  • Specifically, segmentation of the following prostate sectors: peripheral zones, transition zones, central zone, anterior fibromuscular stroma and urethral sphincter
  • Generation/ Refinement of ground truth data
  • Creation of a 3D sector model
  • Test with ProstateX challenge dataset (where zones of findings are given) to predict the zone with Deep Learning
  • if more training data is available: deep learning for an automatic sector segmentation
  • Try atlas-based segmentation approach to map zone segmentations on more datasets
  • Creation of 3 zone segmentations of the prostate (different volumes)
  • Prediction of zones for specific locations in ProstateX dataset with Deep Learning is in progress
  • Next Steps: Create 3D model with higher resolution. Atlas based segmentation on more publicly available data.

Background and References

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