TubeTK/Events/2011.07.06: Difference between revisions
Ilknur.kabul (talk | contribs) No edit summary |
Ilknur.kabul (talk | contribs) No edit summary |
||
| Line 1: | Line 1: | ||
UCLA Meeting Notes: | UCLA Meeting Notes: | ||
== Segmentation in the chronic image == | |||
'''Problem:''' | |||
Tissue classification is available at the chronic time point.Because of diadema, tracking tissue and classifying white and gray matter at the acquired time point is hard. In acute phase, we need to hand segment lesion boundaries. How to label a voxel based on 5 modalities is a problem. | |||
Whether a diadema is hemorrhagic or non-hemorrhagic cannot be generalized. Micro bleeds happens and they are visible in SWI (not visible in the others). | |||
'''Possible approach:''' | |||
With the classification in the chronic image, acute image and Gabe&Marc`s algorithm, can this be done (segmentation) in the acute image? | With the classification in the chronic image, acute image and Gabe&Marc`s algorithm, can this be done (segmentation) in the acute image? | ||
Kitware can do multivariant classification. PDFSegmenter is available in TubeTK. This method is semiautomatic, multivariant, and multiclass (2 types of diadema & tumor). In this approach, you should indicate couple of points in diadema and some other points. Andrei told that Marcel has something similar to that one. Both can be tried and the results can be compared. | |||
'''Input:''' | |||
# Data: At least 4-5 channels (T1, T2, SWI, DTI, GRE) are available. They are good for differenting diademas. | |||
# Lesion classes: How to distinguish between lesion classes? Andrei has a submitted paper about that. | |||
Vocabulary: | |||
hemorrhage: Excessive discharge of blood from the blood vessels; profuse bleeding | |||
Revision as of 13:11, 7 July 2011
UCLA Meeting Notes:
Segmentation in the chronic image
Problem: Tissue classification is available at the chronic time point.Because of diadema, tracking tissue and classifying white and gray matter at the acquired time point is hard. In acute phase, we need to hand segment lesion boundaries. How to label a voxel based on 5 modalities is a problem. Whether a diadema is hemorrhagic or non-hemorrhagic cannot be generalized. Micro bleeds happens and they are visible in SWI (not visible in the others).
Possible approach: With the classification in the chronic image, acute image and Gabe&Marc`s algorithm, can this be done (segmentation) in the acute image?
Kitware can do multivariant classification. PDFSegmenter is available in TubeTK. This method is semiautomatic, multivariant, and multiclass (2 types of diadema & tumor). In this approach, you should indicate couple of points in diadema and some other points. Andrei told that Marcel has something similar to that one. Both can be tried and the results can be compared.
Input:
- Data: At least 4-5 channels (T1, T2, SWI, DTI, GRE) are available. They are good for differenting diademas.
- Lesion classes: How to distinguish between lesion classes? Andrei has a submitted paper about that.
Vocabulary: hemorrhage: Excessive discharge of blood from the blood vessels; profuse bleeding
