Open sumedhasingla opened 6 years ago
In the first try: We converted 3d image to 2d by taking an average over x, y and z direction. The quality of image obtained in this way is not good. Not a very 3d to 2d conversion.
Example:
1) Average over middle 20% of the slices. 2) Average over all the slices 3) A single middle slice.
OK, so this is not the ITK code, this is just averaging, right?
On Sun, Oct 14, 2018 at 1:30 PM Sumedha Singla notifications@github.com wrote:
In the first try: We converted 3d image to 2d by taking an average over x, y and z direction. The UPMC reports are passed through NLP pipeline to extract 14 disease labels. So that we can run multi labe classification problem on them. The quality of image obtained in this way is very bad. Not a very 3d to 2d conversion.
Example: [image: image] https://user-images.githubusercontent.com/13970739/46919963-45178680-cfb5-11e8-800e-fa839dc55233.png
- Average over middle 20% of the slices.
- Average over all the slices
- A single middle slice.
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No this is just averaging. I am posting new comments with those results.
In second effort we tried sampling few slices 2D slices from 3D CT scan, use multiple slices. There is higher chance of missing the regions with findings in this approach. Not a good approach as well. Some samples are placed at location: /pghbio/dbmi/batmanlab/singla/Image_Text_Project/Data_Image_Slices_ChestXRay_Terms/ Example: p113577_a117962
The subjects in UPMC dataset are not always in same orientation. In third try, we used ITK ITKTwoProjectionRegistration to get 2D projection from 3D CT scan. More details about the method can be found at: http://www.insight-journal.org/browse/publication/784 Git hub link: https://github.com/InsightSoftwareConsortium/ITKTwoProjectionRegistration It is available a external module in ITK. Example
@sumedhasingla
This method can solve the projection problem, please try it:
This is from this paper: EMPHYSEMA QUANTIFICATION ON SIMULATED X-RAYS THROUGH DEEP LEARNING TECHNIQUES
[ ] Convert the 3d volumetric image to 2d x-ray
[ ] Run simple model for feature extraction and classification.
[ ] Extract classification labels from UPMC reports by passing through NLP pipeline and extracting 14 disease labels