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This repository has been archived by the owner on Apr 4, 2023. It is now read-only.
Thank you for your previous reply. I have encountered several problems recently, and I hope to get your advice again Q1:This error occurs when I run the demo.py with the lung CT data(LIDC-IDRI 512 * 512 * 133) :itk::ERROR: Key '0008|103e' does not exist when run demo.py I remember you mentioned the threshold-based algorithm algorithm, is this the cause of the error, or the image dimension, could you tell me how to modify? Q2:The input of RCN network is 128*3, but I want to use different sizes of data, what should I do, modify the network structure or cut the size? Q3:I made some changes to the eval.py and recursive_cascaded_networks.py to visualize the results
(1)add 'real_flow', 'warped_moving', 'image_fixed', 'image_moving' to keys
(2)The following lines are added at the end of the main function for val_subset in val_subsets:
(3)add 'image_moving': img2, in recursive_cascaded_networks.py
I succeeded in getting the visualization results on SLIVER dataset.However, parameter errors occurred in the tests on datasets LiTS and LSPIG. eval.py: error: unrecognized arguments: -v lits and eval.py: error: unrecognized arguments -v lspig --paired
Your suggestions are very important to me and I am looking forward to your reply again.
The text was updated successfully, but these errors were encountered:
Thank you for your previous reply. I have encountered several problems recently, and I hope to get your advice again
Q1:This error occurs when I run the demo.py with the lung CT data(LIDC-IDRI 512 * 512 * 133) :itk::ERROR: Key '0008|103e' does not exist when run demo.py I remember you mentioned the threshold-based algorithm algorithm, is this the cause of the error, or the image dimension, could you tell me how to modify?
Q2:The input of RCN network is 128*3, but I want to use different sizes of data, what should I do, modify the network structure or cut the size?
Q3:I made some changes to the eval.py and recursive_cascaded_networks.py to visualize the results
(1)add 'real_flow', 'warped_moving', 'image_fixed', 'image_moving' to keys
(2)The following lines are added at the end of the main function for val_subset in val_subsets:
output_png_dir = 'E:\gzj\Recursive-Cascaded-Networks\png_results_voxel-5'
os.makedirs(output_png_dir)
for i in range(len(results['real_flow'])):
im_flow = RenderFlow(results['real_flow'][i])
skimage.io.imsave(os.path.join(output_png_dir,'flow'+ str(i) +'.png'), im_flow)
warped_img = results['warped_moving'][i]
show_image(warped_img, os.path.join(output_png_dir,'warped'+ str(i) +'.png'))
fixed_img = results['image_fixed'][i]
show_image(fixed_img, os.path.join(output_png_dir,'fixed'+ str(i) +'.png'))
moving_img = results['image_moving'][i]
show_image(moving_img, os.path.join(output_png_dir,'moving'+ str(i) +'.png'))
(3)add 'image_moving': img2, in recursive_cascaded_networks.py
I succeeded in getting the visualization results on SLIVER dataset.However, parameter errors occurred in the tests on datasets LiTS and LSPIG.
eval.py: error: unrecognized arguments: -v lits and eval.py: error: unrecognized arguments -v lspig --paired
Your suggestions are very important to me and I am looking forward to your reply again.
The text was updated successfully, but these errors were encountered: