Comments (9)
yes , me too. I don't know how to options folder, my nii.gz file all in one folder.
from cnn-ms-lesion-segmentation.
Following the provided example notebook, each training image is inside a folder:
train_folder = '/mnt/DATA/w/CNN/images/train_images'
train_x_data = {}
train_y_data = {}
# TRAIN X DATA
train_x_data['im1'] = {'T1': os.path.join(train_folder,'im1', 'T1.nii.gz'),
'FLAIR': os.path.join(train_folder,'im1', 'T1.nii.gz')}
train_x_data['im2'] = {'T1': os.path.join(train_folder,'im2', 'T1.nii.gz'),
'FLAIR': os.path.join(train_folder,'im2', 'T1.nii.gz')}
train_x_data['im3'] = {'T1': os.path.join(train_folder,'im3', 'T1.nii.gz'),
'FLAIR': os.path.join(train_folder,'im3', 'T1.nii.gz')}
# TRAIN LABELS
train_y_data['im1'] = os.path.join(train_folder,'im1', 'lesion_bin.nii.gz')
train_y_data['im2'] = os.path.join(train_folder,'im2', 'lesion_bin.nii.gz')
train_y_data['im3'] = os.path.join(train_folder,'im3', 'lesion_bin.nii.gz')
So, your input folder should be something like:
[training_folder]
[im1 folder]
flair.nii.gz
t1.nii.gz
lesion_bin.nii.gz
[im2 folder]
flair.nii.gz
t1.nii.gz
lesion_bin.nii.gz
...
[im_n folder]
flair.nii.gz
t1.nii.gz
lesion_bin.nii.gz
where image folders names are used as keys in the dictionary train_x_data
Take into account that you can call the training images differently simply filling the dictionary in other ways. For instance, if you have all training mages inside the same folder, then:
train_folder = '/path/to/training/images/'
train_x_data = {}
train_y_data = {}
# TRAIN X DATA
train_x_data['im1_id'] = {'T1': os.path.join(train_folder, 'T1_im1.nii.gz'),
'FLAIR': os.path.join(train_folder, 'FLAIR_im1.nii.gz')}
train_x_data['im2_id'] = {'T1': os.path.join(train_folder, 'T1_im2.nii.gz'),
'FLAIR': os.path.join(train_folder, 'FLAIR_im2.nii.gz')}
train_x_data['im3_id'] = {'T1': os.path.join(train_folder, 'T1_im3.nii.gz'),
'FLAIR': os.path.join(train_folder, 'FLAIR_im3.nii.gz')}
# TRAIN LABELS
train_y_data['im1_id'] = os.path.join(train_folder, 'lesion_bin_im1.nii.gz')
train_y_data['im2_id'] = os.path.join(train_folder,'lesion_bin_im2.nii.gz')
train_y_data['im3_id'] = os.path.join(train_folder,'lesion_bin_im3.nii.gz')
hope it helps,
from cnn-ms-lesion-segmentation.
I'm sorry to make you misunderstand. Actually, I've already put "traning_folder" Follow the instructions.
But I do not know where to find Traning images?
from cnn-ms-lesion-segmentation.
I'm sorry but the repository do not provide training images.
from cnn-ms-lesion-segmentation.
I change folder in my computer, don't in code, than have another error
Traceback (most recent call last):
File "/home/kmust/cnnsg/train_leave_one_out.py", line 79, in
model = train_cascaded_model(model, train_x_data, train_y_data, options)
File "/home/kmust/cnnsg/base.py", line 42, in train_cascaded_model
X, Y = load_training_data(train_x_data, train_y_data, options)
File "/home/kmust/cnnsg/base.py", line 140, in load_training_data
y_data = [train_y_data[s] for s in scans]
KeyError: 'im1'
from cnn-ms-lesion-segmentation.
And what's the question?
It's obvious that something is wrong with the dictionary, isn't it? The dictionary keys should correspond to the image folder names. Just with this information is very difficult to help:
Look at this:
https://github.com/sergivalverde/cnn-ms-lesion-segmentation/blob/master/train_leave_one_out.py#L44
from cnn-ms-lesion-segmentation.
It appears that the data is not in .nii.gz format, but it seems in NRRD. Can someone confirm this?
from cnn-ms-lesion-segmentation.
from cnn-ms-lesion-segmentation.
OK, thanks! I was able to perform the conversion.
from cnn-ms-lesion-segmentation.
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