Difference between revisions of "2014 Project Week:RT FormatConversions"
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John Evans (MGH) | John Evans (MGH) | ||
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==Project Description== | ==Project Description== | ||
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<h3>Progress</h3> | <h3>Progress</h3> | ||
| − | * | + | * Creates python script (based on Slicer RT) for converting contours to label maps (thanks Csaba!) |
| + | * Reporting module provides conversion from label map to DICOM-SEG | ||
| + | * Plastimatch has DICOM-RT import export | ||
| + | * Will test full roundtrip conversions using QIN lung segmentation challenge datasets | ||
| + | * Started creating a library of test cases with known issues (donuts, overlapping structures etc) | ||
| + | * Will also create synthetic datasets | ||
</div> | </div> | ||
</div> | </div> | ||
Latest revision as of 04:54, 10 January 2014
Home < 2014 Project Week:RT FormatConversionsKey Investigators
Jayashree Kalpathy-Cramer (MGH)
Andras
Csaba
John Evans (MGH)
Greg Sharp (MGH)
Project Description
Objective
- Evaluate different options for converting from DICOM-seg/NIFTI/nrrd (mask) to DICOM-RT (contours) and back.
- Compare performance of Slicer RT/plastimatch/others.
Approach, Plan
- Develop a test set with range of appearance of structures
- overlapping
- donuts
Progress
- Creates python script (based on Slicer RT) for converting contours to label maps (thanks Csaba!)
- Reporting module provides conversion from label map to DICOM-SEG
- Plastimatch has DICOM-RT import export
- Will test full roundtrip conversions using QIN lung segmentation challenge datasets
- Started creating a library of test cases with known issues (donuts, overlapping structures etc)
- Will also create synthetic datasets
