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Python 2.7? Really?
Python 2.7 is scheduled for discontinuation. It cannot therefore be integrated into any modern project. Please ensure the code works with a current version of Python, i.e. 3.7 instead.
Untrack .pyc files
.pyc files should never be tracked in git. They're to be untracked from git. Add it to a .gitignore exclusion file. Refer to https://stackoverflow.com/questions/26021040/remove-pyc-files-from-git-remote-repository
Incorporate into related toolkits
Your approach is interesting. Please consider having it incorporated into the following toolkits if you haven't already:
https://github.com/NifTK/NiftyNet
https://github.com/perone/medicaltorch
https://github.com/pfjaeger/medicaldetectiontoolkit
Doing this can dramatically increase the audience of your approach. Thanks.
Here is an error about 'inception_deep '
def inception_deep(self,deph,X):
Y1 = Conv3D(deph, 1, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(X)
Y1 = self.drop(Y1)
Y1 = Conv3D(deph, (1,1,7), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y1)
Y1 = Conv3D(deph, (1,7,1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y1)
Y1 = Conv3D(deph, (7,1,1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y1)
Y2 = Conv3D(deph, 1, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(X)
Y2 = self.drop(Y2)
Y2 = Conv3D(deph, (1,1,7), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y2)
Y3 = Conv3D(deph, (1,7,1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y2)
Y4 = Conv3D(deph, (7,1,1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y2)
Y5 = Conv3D(deph, 1, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(X)
Y5 = self.drop(Y5)
Y5 = Conv3D(deph, 5, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y5)
Y = Concatenate()([Y1,Y2,Y3,Y4,Y5])
return Y
Y4 and Y3 is not the output we expect.
fix:
def inception_deep(self,deph,X):
Y1 = Conv3D(deph, 1, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(X)
Y1 = self.drop(Y1)
Y1 = Conv3D(deph, (1,1,7), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y1)
Y1 = Conv3D(deph, (1,7,1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y1)
Y1 = Conv3D(deph, (7,1,1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y1)
Y2 = Conv3D(deph, 1, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(X)
Y2 = self.drop(Y2)
Y2_output = Conv3D(deph, (1,1,7), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y2)
Y3 = Conv3D(deph, (1,7,1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y2)
Y4 = Conv3D(deph, (7,1,1), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y2)
Y5 = Conv3D(deph, 1, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(X)
Y5 = self.drop(Y5)
Y5 = Conv3D(deph, 5, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal',use_bias=False)(Y5)
Y = Concatenate()([Y1,Y2_output,Y3,Y4,Y5])
return Y
the original volume data and the groud truth
I really appreciate your work. Your work is outstanding.
Could you please send me the original volume data and the groud truth of cerebrovascular MRA ?My email address is [email protected]
Thank you very much!
Fix broken image link in readme
The readme embeds an image with a bad link. Please fix the link.
Dataset used in the project sharing
Hi, thanks for your sharing with your code. I'm doing the same research as your project.But I have not got the dataset to train my model. I'll appreciate it if you can share your dataset used in the project with me.
Thank you!
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