Hi, I am working on the Brats-20 dataset. I want to create a gif animation of labels on images and prediction results on images. I am trying to do that. However, I am confused about the plots I get from the visualization.
I use the Keras function 'to_categorical' to convert labels to one hot representation from (240, 240, 155) shape as in the following code.
`def categorical(image, mask, is_categorical=False):
if not is_categorical:
label = to_categorical(mask, num_classes = 4).astype(np.uint8)
image = cv2.normalize(image[:, :, :, 0], None, alpha=0, beta=255,
norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F).astype(np.uint8)
labeled_image = np.zeros_like(label[:, :, :, 1:])
labeled_image[:, :, :, 0] = image * (label[:, :, :, 0])
labeled_image[:, :, :, 1] = image * (label[:, :, :, 0])
labeled_image[:, :, :, 2] = image * (label[:, :, :, 0])
# color labels
labeled_image += label[:, :, :, 1:] * 255
return labeled_image`
Here the image has the shape of (240, 240, 155, 4) corresponding to a patient scan, and the mask has the shape of (240, 240, 155).
I want to create a nice visualization like that:
And a gif file to show labels or prediction results on an image. But the _seg.nii.gz (label file) contains the number 4 in the tensor. Which gives me an error in to_categorical. Because I am using num_classes =4 in the above function as you can see. Then I normalized the labels which converted labels for int to float values, and it was wrong.
Then I wrote something like limiting pixel labels from 1 to 3 to remove the error. Which successfully removed the error, however, I don't know if it is correct to label colors for the visualization. OR has it been mapped accurately or not? I need guidance, please.
I wrote the following code to map labels from 1 to 3 (I don't know if it's correct or not), but it removed the error in the to_categorical function. The code:
`def normalize(mask: np.ndarray):
# data_min = np.min(data)
# return (data - data_min) / (np.max(data) - data_m
mask[mask == 1] = 1
mask[mask == 2] = 2
# mask[mask == 3] = 0
mask[mask == 4] = 3
return mask`
Help will be appreciated as I am stuck here.
Hi, I am working on the Brats-20 dataset. I want to create a gif animation of labels on images and prediction results on images. I am trying to do that. However, I am confused about the plots I get from the visualization.
I use the Keras function 'to_categorical' to convert labels to one hot representation from (240, 240, 155) shape as in the following code.
`def categorical(image, mask, is_categorical=False):
if not is_categorical:
label = to_categorical(mask, num_classes = 4).astype(np.uint8)
Here the image has the shape of (240, 240, 155, 4) corresponding to a patient scan, and the mask has the shape of (240, 240, 155).
I want to create a nice visualization like that:
And a gif file to show labels or prediction results on an image. But the _seg.nii.gz (label file) contains the number 4 in the tensor. Which gives me an error in to_categorical. Because I am using num_classes =4 in the above function as you can see. Then I normalized the labels which converted labels for int to float values, and it was wrong.
Then I wrote something like limiting pixel labels from 1 to 3 to remove the error. Which successfully removed the error, however, I don't know if it is correct to label colors for the visualization. OR has it been mapped accurately or not? I need guidance, please.
I wrote the following code to map labels from 1 to 3 (I don't know if it's correct or not), but it removed the error in the to_categorical function. The code:
`def normalize(mask: np.ndarray):
Help will be appreciated as I am stuck here.