Difference between revisions of "2016 Winter Project Week/Projects/CongenitalHeart"

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==Project Description==
 
==Project Description==
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* We have developed an interactive segmentation algorithm for cardiac MR images from patients with severe congenital heart disease, in which the user manually segments a few short-axis slices and the remainder is segmented automatically using patch-based segmentation (Pace et al, MICCAI 2015)
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! style="text-align: left; width:27%" |  Objective
 
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* Continue improving the method, especially focusing on correctly segmenting thin walls of the heart when few slices are manually segmented
 
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* Continue investigating algorithm modifications to enhance / correctly segment the thin walls.
 
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* Worked on integrating new "thin wall" features to improve thin wall detection
 
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==Background and References==
 
==Background and References==
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<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data -->
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* [http://link.springer.com/chapter/10.1007/978-3-319-24574-4_10 Pace et al,, Interactive Whole-Heart Segmentation in Congenital Heart Disease, MICCAI 2015]

Latest revision as of 15:01, 8 January 2016

Home < 2016 Winter Project Week < Projects < CongenitalHeart

  • Results after user manually segments 3, 8 or 14 slices

Key Investigators

  • Danielle Pace (MIT)
  • Adrian Dalca (MIT)
  • Polina Golland (MIT)

Project Description

  • We have developed an interactive segmentation algorithm for cardiac MR images from patients with severe congenital heart disease, in which the user manually segments a few short-axis slices and the remainder is segmented automatically using patch-based segmentation (Pace et al, MICCAI 2015)
Objective Approach and Plan Progress and Next Steps
  • Continue improving the method, especially focusing on correctly segmenting thin walls of the heart when few slices are manually segmented
  • Continue investigating algorithm modifications to enhance / correctly segment the thin walls.
  • Worked on integrating new "thin wall" features to improve thin wall detection

Background and References

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