Difference between revisions of "2014 Summer Project Week:Cardiac-Congenital"

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==Project Description==
 
==Project Description==
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This project involves semi-automatic segmentation of gated 3D magnetic resonance images of hearts with congenital heart defects.  Our aim is to create patient-specific heart models for surgical planning, which can be viewed either graphically on a computer, or with a 3D printer to create a physical model for surgeons.
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We have had initial success in significantly reducing segmentation time with the following pipeline:
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1) User manually segments ~10 axial slices
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2) Segment the remaining slices using patch-based majority voting.
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<h3>Objective</h3>
 
<h3>Objective</h3>
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* Continue testing the patch based methods on an additional five datasets.
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* Address main remaining challenge: segmenting thin interior heart walls.
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
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* Manually segment ~10 axial slices for each of the five additional datasets.
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* Explore remaining parameters for the patch-based segmentation: e.g. weighted voting, varying k in k-nearest neighbors patch lookup
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Revision as of 16:03, 23 June 2014

Home < 2014 Summer Project Week:Cardiac-Congenital

Key Investigators

  • Danielle Pace, MIT
  • Adrian Dalca, MIT
  • Polina Golland, MIT

Project Description

This project involves semi-automatic segmentation of gated 3D magnetic resonance images of hearts with congenital heart defects. Our aim is to create patient-specific heart models for surgical planning, which can be viewed either graphically on a computer, or with a 3D printer to create a physical model for surgeons.

We have had initial success in significantly reducing segmentation time with the following pipeline: 1) User manually segments ~10 axial slices 2) Segment the remaining slices using patch-based majority voting.

Objective

  • Continue testing the patch based methods on an additional five datasets.
  • Address main remaining challenge: segmenting thin interior heart walls.

Approach, Plan

  • Manually segment ~10 axial slices for each of the five additional datasets.
  • Explore remaining parameters for the patch-based segmentation: e.g. weighted voting, varying k in k-nearest neighbors patch lookup

Progress