Difference between revisions of "NeedleFinder"
From NAMIC Wiki
| Line 7: | Line 7: | ||
==Key Investigators== | ==Key Investigators== | ||
| + | Andre Mastmeyer, | ||
| + | Guillaume Pernelle, | ||
| + | Tina Kapur | ||
| − | + | ==Project Description (Draft by Andre)== | |
| − | ==Project Description== | ||
<div style="margin: 20px;"> | <div style="margin: 20px;"> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Objective</h3> | <h3>Objective</h3> | ||
| − | * | + | * Automatic needle tip detection |
| + | * Modelfit of oburator base and tube using CAD models | ||
| + | * NF detection parameter optimization | ||
| + | * Code profiling (is the code used?), refactoring | ||
| + | * GUI improvements | ||
</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> | ||
| − | * | + | * SimpleITK filtering (e.g. derivatives: gradient magnitude, vesselness etc.) |
| + | * Rigid registration of CAD models to (preprocessed) scene (ICP, Besl) | ||
| + | * Machine learning machine learning (genetic algorithm etc.) | ||
| + | * New ad-hoc python profiling concept (logging and message boxes as code probes) | ||
| + | * Improved standardized workflow more usable by MDs | ||
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Progress</h3> | <h3>Progress</h3> | ||
| − | * | + | * N/Y |
| + | * N/Y | ||
| + | * N/Y | ||
| + | * N/Y | ||
| + | * N/Y | ||
</div> | </div> | ||
</div> | </div> | ||
Revision as of 18:52, 16 December 2014
Home < NeedleFinderHere we will fill in tomorrow...
Key Investigators
Andre Mastmeyer, Guillaume Pernelle, Tina Kapur
Project Description (Draft by Andre)
Objective
- Automatic needle tip detection
- Modelfit of oburator base and tube using CAD models
- NF detection parameter optimization
- Code profiling (is the code used?), refactoring
- GUI improvements
Approach, Plan
- SimpleITK filtering (e.g. derivatives: gradient magnitude, vesselness etc.)
- Rigid registration of CAD models to (preprocessed) scene (ICP, Besl)
- Machine learning machine learning (genetic algorithm etc.)
- New ad-hoc python profiling concept (logging and message boxes as code probes)
- Improved standardized workflow more usable by MDs
Progress
- N/Y
- N/Y
- N/Y
- N/Y
- N/Y
