Running testing.py about 3dunetcnn HOT 22 CLOSED

@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.

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Alright!

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With the pre-trained model given here I generated training_ids.pkl andtesting_ids.pkl.Bascially just to get testing ids required to test the model. I defined main class as :

if __name__ == '__main__':
    run_test_case(1,"./data_test")

The problem is it is not producing any data_dir .Neither it is appending to it if I create one. And this:

if __name__ == '__main__':
    model = load_model(config["model_file"])
    predict_from_data_file_and_write_image(model,config["hdf5_file"],0,result.nii.gz)

And this

if __name__ == '__main__':
    model = load_model(config["model_file"])
    predict_from_data_file_and_write_image(model,config["hdf5_file"],0,data_dir)#data_dir  is directory

All of them fail to produce any segmented output. Plus where is get_prediction_labels getting called?
@ellisdg any idea on this one? Apologies for the constant trouble.

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Maybe try giving a full path name instead of "./data_test"? Does that work?

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It worked but gave error
ValueError: "concat" mode can only merge layers with matching output shapes except for the concat axis. Layer shapes: [(None, 1024, 18, 18, 9), (None, 256, 18, 18, 18)]
then I updated the configuration in keras.json file as

{
"image_dim_ordering": "th",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "tensorflow"
}

Note: earlier "backend": "theano" this was the in the keras.json file so it was giving error.(refer this)
it ran finally but gave error:
ValueError: Invalid objective: dice_coef_loss

then I added these:
from model import dice_coef_loss
from model import dice_coef
and
model = load_model(config["model_file"],custom_objects={'dice_coef_loss': dice_coef_loss,'dice_coef':dice_coef})

Now I get this error:

ValueError: Error when checking : expected input_1 to have 5 dimensions, but got array with shape (3, 144, 144, 144)
To which I added 'T2' to config["training_modalities"]
Now to the call in main:
run_test_case(1,'/home/adminsters/Desktop/3DUnetCNN-master/data_dir');
it gives index error:

  File "testing.py", line 67, in <module>
    run_test_case(1,'/home/adminsters/Desktop/3DUnetCNN-master/data_dir');
  File "testing.py", line 53, in run_test_case
    image = nib.Nifti1Image(test_data[i], affine)
IndexError: index 3 is out of bounds for axis 0 with size 3

My testing_ids.pkl is:
[141, 20, 15, 57, 186, 167, 60, 80, 23, 54, 143, 158, 132, 83, 61, 81, 168, 85, 157, 90, 147, 86, 127, 199, 74, 136, 24, 182, 146, 152, 170, 44, 30, 156, 172, 154, 1, 25, 59, 45]

I guess it is because I changed the config["training_modalities"]
@ellisdg how to fix this Index error?

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I am getting index error. Please see updated comment

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In case you are not able to see I am reposting it here:
Initially I was getting this:
ValueError: "concat" mode can only merge layers with matching output shapes except for the concat axis. Layer shapes: [(None, 1024, 18, 18, 9), (None, 256, 18, 18, 18)]
then I updated the configuration in keras.json file as

{
"image_dim_ordering": "th",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "tensorflow"
}

Note: earlier"backend": "theano" this was the in the keras.json file so it was giving error.(refer this)
it ran finally but gave error:
ValueError: Invalid objective: dice_coef_loss
then I added these:

from model import dice_coef_loss
from model import dice_coef

and
model = load_model(config["model_file"],custom_objects={'dice_coef_loss': dice_coef_loss,'dice_coef':dice_coef})
Now I get this error:

ValueError: Error when checking : expected input_1 to have 5 dimensions, but got array with shape (3, 144, 144, 144)
So in config.py I added 'T2' to config["training_modalities"]
Now to the call in main:
run_test_case(1,'/home/adminsters/Desktop/3DUnetCNN-master/data_dir');
it gives index error:

  File "testing.py", line 67, in <module>
    run_test_case(1,'/home/adminsters/Desktop/3DUnetCNN-master/data_dir');
  File "testing.py", line 53, in run_test_case
    image = nib.Nifti1Image(test_data[i], affine)
IndexError: index 3 is out of bounds for axis 0 with size 3

My testing_ids.pkl is:
[141, 20, 15, 57, 186, 167, 60, 80, 23, 54, 143, 158, 132, 83, 61, 81, 168, 85, 157, 90, 147, 86, 127, 199, 74, 136, 24, 182, 146, 152, 170, 44, 30, 156, 172, 154, 1, 25, 59, 45]

I guess it is because I changed the config["training_modalities"]
@ellisdg how to fix this Index error?

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It looks like you may have modified line 53, or you may not have the latest version of the file.

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Ok my bad, ran the updated code this time.
I got his output :

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
When I choose to open segmented image I get this:

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?

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If I have understood it right this is not for tumor detection but just for volumetric segmentation right?

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For FLAIR modality I am sort of getting evidently slightly more greyish patch, Is that the tumor?

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T1:

T1c:

FLAIR:

Tumor prediction. Ranges from 0 to 1:

Manual segmentation. The separate tumor regions are not used by the classifier:

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Thanks. By the way which tool are you using for viewing? Is it ITK sanp?

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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.

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Thanks a lot!

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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?

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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.

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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.

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for anyone coming from the future, testing.py was renamed to predict.py:

b4012bb#diff-22508acf6c6356eb94fbcda75eaf09f2

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