Difference between revisions of "2012 Winter Project Week:PairwiseLF"
From NAMIC Wiki
(Created page with '__NOTOC__ <gallery> Image:PW-SLC2012.png|Projects List </gallery> ==Investigators == * Ramesh Sridharan * Christian Wachinger * Polina Goll…') |
|||
| (2 intermediate revisions by the same user not shown) | |||
| Line 13: | Line 13: | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Objective</h3> | <h3>Objective</h3> | ||
| − | We will | + | Our goal is to improve the performance of label fusion for high-variability datasets in which registration algorithms may not align subjects very well. We will investigate two directions for improving label fusion: |
| + | * We will examine the potential of using parameters learned over the label fusion training set to improve the quality of segmentation | ||
| + | * We will examine pairwise interactions between images in the training set for label fusion. | ||
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Approach, Plan</h3> | <h3>Approach, Plan</h3> | ||
| − | + | Our approach is to learn intensity and label prediction parameters over pairs of images in the training set. | |
| + | |||
| + | Our plan for the Project Week is to apply our method to MR and CT data, including the Head and Neck Cancer dataset. | ||
</div> | </div> | ||
| Line 25: | Line 29: | ||
<h3>Progress</h3> | <h3>Progress</h3> | ||
| − | * | + | * Discussed data and motivation with fellow project week participants |
| − | + | * Discussed registration issues with data | |
| + | * Continued to apply method to synthetic data | ||
</div> | </div> | ||
<div style="width: 97%; float: left;"> | <div style="width: 97%; float: left;"> | ||
Latest revision as of 00:14, 13 January 2012
Home < 2012 Winter Project Week:PairwiseLF
Investigators
- Ramesh Sridharan
- Christian Wachinger
- Polina Golland
Objective
Our goal is to improve the performance of label fusion for high-variability datasets in which registration algorithms may not align subjects very well. We will investigate two directions for improving label fusion:
- We will examine the potential of using parameters learned over the label fusion training set to improve the quality of segmentation
- We will examine pairwise interactions between images in the training set for label fusion.
Approach, Plan
Our approach is to learn intensity and label prediction parameters over pairs of images in the training set.
Our plan for the Project Week is to apply our method to MR and CT data, including the Head and Neck Cancer dataset.
Progress
- Discussed data and motivation with fellow project week participants
- Discussed registration issues with data
- Continued to apply method to synthetic data
