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Running testing.py #7
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@build2create sorry, I hadn't tested that code out yet. With the latest commit that I just committed, you should be able to run the code as you have it in your comment. |
Alright! |
With the pre-trained model given here I generated
The problem is it is not producing any
And this
All of them fail to produce any segmented output. Plus where is |
Maybe try giving a full path name instead of "./data_test"? Does that work? |
It worked but gave error
Note: earlier then I added these: Now I get this error:
My testing_ids.pkl is: I guess it is because I changed the |
There are no error messages? Are you sure it is running? Have you tried a
debugger?
…On Apr 20, 2017 11:47 PM, "build2create" ***@***.***> wrote:
Nope, it did not work
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I am getting index error. Please see updated comment |
In case you are not able to see I am reposting it here:
Note: earlier"backend": "theano" this was the in the keras.json file so it was giving error.(refer this)
and
My testing_ids.pkl is: I guess it is because I changed the |
It looks like you may have modified line 53, or you may not have the latest version of the file. |
Ok my bad, ran the updated code this time. |
The image is a scalar image and not a label map. You need to view it in
grayscale as a scalar image.
…On Apr 22, 2017 12:24 AM, "build2create" ***@***.***> wrote:
Ok my bad, ran the updated code this time.
I got his output :
[image: image]
<https://cloud.githubusercontent.com/assets/25721143/25301595/219251ba-2749-11e7-87a8-0dd95880fda5.png>
for a test image id 20. How should I infer segmentation labels from this,
which one is necrotic,edemic and so on? Like the one in this
<https://github.com/naldeborgh7575/brain_segmentation>
When I choose to open segmented image I get this:
[image: image]
<https://cloud.githubusercontent.com/assets/25721143/25301629/d02a263a-2749-11e7-86a1-ddaf4b51fbb8.png>
Which patch is tumorous how should I interpret?
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Can you elaborate? The first screenshot shows grayscale image. Is that correct?How do I read the first image?Is it like darker parts tumorous? Can you cite/give an example? |
If I have understood it right this is not for tumor detection but just for volumetric segmentation right? |
For FLAIR modality I am sort of getting evidently slightly more greyish patch, Is that the tumor? |
Thanks. By the way which tool are you using for viewing? Is it ITK sanp? |
3D Slicer. I know a lot of people use ITK Snap, but I've never taken the time to learn how to use it. 3D Slicer has a lot of features, so there might be a little bit of a learning curve. They also have plenty of helpful tutorials too. |
Thanks a lot! |
By volumetric segmentation we are detecting the presence or absence of the tumor but not the different segments within the tumor, like necrotic, core or enhancing. Doing this would be a multi-class classification problem right? |
Also why are we not able to segment the tumor into sub-parts.Is it because of data set?I viewed ground truth of BRATS dataset in mha format. They do provide all the segmentation labels. |
You should be able to train a classifier to segment different tumor regions with Keras. However, I currently have no need for multi-class labels, so I have not implemented it yet. I will probably add this eventually, but that could be months from now. |
for anyone coming from the future, testing.py was renamed to predict.py: |
Some large resolutions require batch size 1 for inferencing as well as training.
Some large resolutions require batch size 1 for inferencing as well as training.
Will be this be correct way to the
testing.py
where
./data_test
is the name to theout_dir
where theprediction.nii.gz
should I also callpredict_from_data_file_and_write_image
fromrun_test_case()
to actually write the image?The text was updated successfully, but these errors were encountered: