Difference between revisions of "2017 Winter Project Week/Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery"

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File:USandMRIimagesMLSegmentation.png|Several structures of the brain segmented from US- and MR images.
 
File:USandMRIimagesMLSegmentation.png|Several structures of the brain segmented from US- and MR images.
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* Jennifer Nitsch, University of Bremen (Germany)
 
* Jennifer Nitsch, University of Bremen (Germany)
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* Hans Meine, University of Bremen (Germany)
  
  
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* Multi-Modual Image Segmentation of preoperative MR- and intraoperative Ultrasound(US)-images
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* Multi-Modual Image Segmentation of preoperative MR- and intraoperative Ultrasound(US)-images for multi-modual image registration.
 
* Segmentation of the following anatomical structures: Falx cerebri, tentorium cerebelli, white matter, gray matter, CSF (ventricles), blood vessels.
 
* Segmentation of the following anatomical structures: Falx cerebri, tentorium cerebelli, white matter, gray matter, CSF (ventricles), blood vessels.
 
* Testing applicability of Deep Learing on current data  
 
* Testing applicability of Deep Learing on current data  
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*Trained a DNN for skull stripping on 18 data sets from a public database, also segmented in GM, WM, CSF
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*There a a lot of databases for T1-weighted brain images, but good reference data is rare...
 
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==Background and References==
 
==Background and References==
 
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Latest revision as of 14:37, 16 January 2017

Home < 2017 Winter Project Week < Multi-ModalitySegmentationOfUSandMRImagesForGliomaSurgery

Key Investigators

  • Jennifer Nitsch, University of Bremen (Germany)
  • Hans Meine, University of Bremen (Germany)


Project Description

Objective Approach and Plan Progress and Next Steps
  • Multi-Modual Image Segmentation of preoperative MR- and intraoperative Ultrasound(US)-images for multi-modual image registration.
  • Segmentation of the following anatomical structures: Falx cerebri, tentorium cerebelli, white matter, gray matter, CSF (ventricles), blood vessels.
  • Testing applicability of Deep Learing on current data
  • Generation/ Refinement of ground truth data
  • Generation/Creation/Refinement of characteristic (anatomical) landmarks (e.g. charactersitic representation of cerebellum, insula cortex (lateral sulcus), choroid plexus.
  • Prepare images for Deep Learning and seeting up Deep learning infrastructure.
  • Trained a DNN for skull stripping on 18 data sets from a public database, also segmented in GM, WM, CSF
  • There a a lot of databases for T1-weighted brain images, but good reference data is rare...

Background and References

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