Difference between revisions of "2013 Summer Project Week:WMH Segmentation for Stroke"
(Created page with '__NOTOC__ <gallery> Image:PW-SLC2013.png|Projects List Image:WMH_T1.png| T1 images in stroke dataset. Image:WMHseg.png | left: FLAIR ima…') |
|||
| Line 5: | Line 5: | ||
Image:WMH_T1.png| T1 images in stroke dataset. | Image:WMH_T1.png| T1 images in stroke dataset. | ||
Image:WMHseg.png | left: FLAIR images, middle: manual delineation of relevant areas, right: manual WMH segmentation. | Image:WMHseg.png | left: FLAIR images, middle: manual delineation of relevant areas, right: manual WMH segmentation. | ||
| + | Image:WMHsegCor.jpg | Illustrating the difference between periventricular WMH (red selection) and other hyperintense issue not part of WMH (yellow outline) | ||
</gallery> | </gallery> | ||
Revision as of 03:52, 17 June 2013
Home < 2013 Summer Project Week:WMH Segmentation for Stroke
T1 images in stroke dataset.
left: FLAIR images, middle: manual delineation of relevant areas, right: manual WMH segmentation.
Illustrating the difference between periventricular WMH (red selection) and other hyperintense issue not part of WMH (yellow outline)
Key Investigators
- Adrian Dalca, Ramesh Sridharan, Polina Golland, MIT
- Natalia Rost, Jonathan Rosand, MGH
Project Description
Objective
Following our method developed for segmentation of white matter hyperintensity (WMH) in FLAIR (discussed at the 2013 project week), we are developing methods for detecting regions that present similar to WMH but represent tissue damaged from a previous stroke (chronic stroke) or other processes. These do not have specific shape, but are in general larger and not necessarily periventricular as the WMH tends to be. This dataset is particularly challenging due to the low resolution (1mm x 1mm x 7mm) and with cropped fields of view in the given images.
Approach, Plan
We have a current pipeline for registration of this clinical dataset, and subsequent detection and investigation of WMH patterns..
- Investigate the number and shapes of 'chronic strokes' lesions, as well as the distribution pattern (perhaps to be used a prior in our models below)
- Learn features that can determine
- Solidify a model, currently considering a model with 4 mixture groups.
- Solve and predict using the model (probably a longer goal)
