Giter Club home page Giter Club logo

deep_mri_brain_extraction's People

Contributors

gur9000 avatar jensnrad avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

deep_mri_brain_extraction's Issues

Some question about code

In Segmentation_predictor line 132
pred[c, x::CNET_stride[0], y::CNET_stride[1], z::CNET_stride[2]] = rr[0,:,c,:,:].reshape((pred_size[0], pred_size[1], pred_size[2]))

I really want to know why the pred's value is determined by this. I mean it jumps.
And at the top of this line I notice that you only shift the data one vixel but you can get twice output. I think it is overlap. Maybe I misunderstand your paper? I am really confused.

RuntimeError: error getting worksize: CUDNN_STATUS_BAD_PARAM

$ python deep3Dpredict.py -name OASIS_ISBR_LPBA40__trained_CNN.save -data TCGA-02-0046_1998.11.28_t2.nii.gz -output deep-mask.nii -gridsize 16 
WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.
Using cuDNN version 6021 on context None
Mapped name None to device cuda: GeForce GTX 1080 (0000:01:00.0)
/home/mingrui/anaconda3/envs/p2_3DUnetCNN/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
using model-parameters: OASIS_ISBR_LPBA40__trained_CNN.save
loading...
Loaded... 100.0 % (240, 1, 240, 155)  ('TCGA-02-0046_1998.11.28_t2.nii.gz', None)
Total n. of examples: 1 images/volumes
Training on 0 images/volumes
Testing on  1 images/volumes
Building CNN...
Traceback (most recent call last):
  File "deep3Dpredict.py", line 200, in <module>
    main()
  File "deep3Dpredict.py", line 194, in main
    network_size_factor = float(args.CNN_width_scale))
  File "deep3Dpredict.py", line 116, in predict
    auto_threshold_labels = auto_threshold_labels)
  File "/mnt/960EVO/workspace/Deep_MRI_brain_extraction/utils/Segmentation_trainer.py", line 232, in Build3D
    dense_output_from_fragments = is_last_layer and use_fragment_pooling)
  File "/mnt/960EVO/workspace/Deep_MRI_brain_extraction/NNet_Core/NN_ConvNet.py", line 350, in addConvLayer
    output_stride = self.output_stride, verbose = self.verbose)
  File "/mnt/960EVO/workspace/Deep_MRI_brain_extraction/NNet_Core/NN_ConvLayer_3D.py", line 271, in __init__
    output_shape = list(  theano.function([input], self.output.shape, mode = self.mode)(numpy.zeros((1 if input_shape[0]==None else input_shape[0],)+input_shape[1:],dtype=numpy.float32)))
  File "/home/mingrui/anaconda3/envs/p2_3DUnetCNN/lib/python2.7/site-packages/theano/compile/function_module.py", line 917, in __call__
    storage_map=getattr(self.fn, 'storage_map', None))
  File "/home/mingrui/anaconda3/envs/p2_3DUnetCNN/lib/python2.7/site-packages/theano/gof/link.py", line 325, in raise_with_op
    reraise(exc_type, exc_value, exc_trace)
  File "/home/mingrui/anaconda3/envs/p2_3DUnetCNN/lib/python2.7/site-packages/theano/compile/function_module.py", line 903, in __call__
    self.fn() if output_subset is None else\
RuntimeError: error getting worksize: CUDNN_STATUS_BAD_PARAM
Apply node that caused the error: GpuDnnConv{algo='small', inplace=True, num_groups=1}(GpuContiguous.0, GpuContiguous.0, GpuAllocEmpty{dtype='float32', context_name=None}.0, GpuDnnConvDesc{border_mode='valid', subsample=(1, 1), dilation=(1, 1), conv_mode='conv', precision='float32', num_groups=1}.0, Constant{1.0}, Constant{0.0})
Toposort index: 26
Inputs types: [GpuArrayType<None>(float32, (False, True, False, False)), GpuArrayType<None>(float32, (False, True, False, False)), GpuArrayType<None>(float32, 4D), <theano.gof.type.CDataType object at 0x7ff807861bd0>, Scalar(float32), Scalar(float32)]
Inputs shapes: [(83, 1, 83, 83), (64, 1, 4, 4), (83, 64, 80, 80), 'No shapes', (), ()]
Inputs strides: [(27556, 27556, 332, 4), (64, 64, 16, 4), (1638400, 25600, 320, 4), 'No strides', (), ()]
Inputs values: ['not shown', 'not shown', 'not shown', <capsule object NULL at 0x7ff8079ad900>, 1.0, 0.0]
Outputs clients: [[GpuReshape{5}(GpuDnnConv{algo='small', inplace=True, num_groups=1}.0, TensorConstant{[83 16  4 80 80]})]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
  File "deep3Dpredict.py", line 200, in <module>
    main()
  File "deep3Dpredict.py", line 194, in main
    network_size_factor = float(args.CNN_width_scale))
  File "deep3Dpredict.py", line 116, in predict
    auto_threshold_labels = auto_threshold_labels)
  File "/mnt/960EVO/workspace/Deep_MRI_brain_extraction/utils/Segmentation_trainer.py", line 232, in Build3D
    dense_output_from_fragments = is_last_layer and use_fragment_pooling)
  File "/mnt/960EVO/workspace/Deep_MRI_brain_extraction/NNet_Core/NN_ConvNet.py", line 350, in addConvLayer
    output_stride = self.output_stride, verbose = self.verbose)
  File "/mnt/960EVO/workspace/Deep_MRI_brain_extraction/NNet_Core/NN_ConvLayer_3D.py", line 217, in __init__
    filters_shape=filter_shape, signals_shape = input_shape if input_shape[0]!=None else None

HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

I tried using tcia nifti data, this is the error

deep3Dpredict.py does not recognize .save file

There is a bug in deep3Dpredict.py:

python Deep_MRI_brain_extraction/deep3Dpredict.py -n Deep_MRI_brain_extraction/OASIS_ISBR_LPBA40__trained_CNN.save -i  $target -gridsize 16
ValueError: The provided save file/directory does not contain any saved model (file ending in .save)

I tracked the error to the line candidates = findall(path_or_file) which returns

['D', 'e', 'e', 'p', '_', 'M', 'R', 'I', '_', 'b', 'r', 'a', 'i', 'n', '_', 'e', 'x', 't', 'r', 'a', 'c', 't', 'i', 'o', 'n', '/.autofsck', 'O', 'A', 'S', 'I', 'S', '_', 'I', 'S', 'B', 'R', '_', 'L', 'P', 'B', 'A', '4', '0', '_', '_', 't', 'r', 'a', 'i', 'n', 'e', 'd', '_', 'C', 'N', 'N', ...

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.