xf4j / brats17 Goto Github PK

Traceback (most recent call last):
File "/home/ubuntu/new/brats17-master/generate_patches.py", line 54, in
pool.map(batch_works, range(n_processes))
File "/home/ubuntu/anaconda3/envs/py2/lib/python2.7/multiprocessing/pool.py", line 253, in map
return self.map_async(func, iterable, chunksize).get()
File "/home/ubuntu/anaconda3/envs/py2/lib/python2.7/multiprocessing/pool.py", line 572, in get
raise self._value
IndexError: list index out of range

hello,when I run python bias_correction.py Brats17TrainingData,the error occured,can you tell me how to solve it?Many thanks!

Hello, I have installed the nipype 1.0.2 version, but when i run bias_correction.py, it comes that:

IOError: No command "N4BiasFieldCorrection" found on host PC. Please check that the corresponding package is installed.

The latest version of nipype(1.0.2) have already integrated the ants package, and in "nipype.interfaces.ants.segmentation", there also exist "N4BiasFieldCorrection" in it.

Why it still suggest me to check that the corresponding package is installed or not.

Waiting for u reply!
Many thanks!

It seems that the download of data is closed now, could u share the url?

ModuleNotFoundError Traceback (most recent call last)

---> 10 from model import UNet3D, SurvivalVAE
ModuleNotFoundError: No module named 'model'

Hi, could you share a link to the trained model, or model weights
Thanks

I can't understand how to generate patches in the code. can someone help me?

I found u say "Submission for Multimodal Brain Tumor Segmentation Challenge 2017 (http://braintumorsegmentation.org/). A patch-based 3D U-Net model is used. Instead of predicting the class label of the center pixel, this model predicts the class label for the entire patch. A sliding-window method is used in deployment with overlaps between patches to average the predictions."
What is the "Instead of predicting the class label of the center pixel"?
For example,

In training, if I input a 48x48x48 train patch and a 48x48x48 label patch in one "predicting the class label of the center pixel" network, after some conv layers, it may output a 40x40x40 train patch, so I have to crop label patch to 40x40x40 surround center pixel for input?

and in testing, if I also need to add some background pixels surround the test image? Like the training, I need to add 8 pixel surround the the image?
if add 1 pixel, like :
0 0 0 0 0 0
0 2 2 2 2 0
0 2 2 2 2 0
0 0 0 0 0 0

thanks.

The brats2017 website has mentioned you as 3rd winner in the category of survival prediction, then how come you have mentioned that survival prediction task is giving results similar to random guessing?