mrtoct-tensorflow
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bodokaiser
Tensorflow models for MRI to CT synthesis.
I inspected the shape of RIRE dataset. CTs have the dimension (512, 512) with slices ranging within 20 to 50 while MRs have the dimension (256, 256) with a similar...
In provider.py at line 111 ```data.transform.MinMaxNormalization(tf.uint16.max),``` There should be this ```data.transform.MinMaxNormalization(),``` In paper you have mentioned that you are able to perform one iteration of auto-context model so i was...
I have been trying to get started with mrtoct. After some slight modifications, I have had initial success with the data download, extract, convert and coregister, as well as running...
Requires us to use [`tf.layers.Conv3D(..., data_format='channels_first')`][1] and `mrtoct.data.transform` to change between `CDHW` and `DHWC`. [1]: https://www.tensorflow.org/versions/master/api_docs/python/tf/layers/Conv3D
Use `with tf.device('/cpu:0')` for pre processing and `with tf.device('/gpu:0')` for training and inference.
Should improve performance and be simple to enable just by passing `fuse=True` to [`tf.layers.BatchNormalization`][1]. [1]: https://www.tensorflow.org/versions/master/api_docs/python/tf/layers/BatchNormalization
If we use `padding='same'` for the first convolution layer with `kernel_size=9` we get output shape `[batch_size, 16, 16, 16, 1]` as intended from the original paper.
Requires consent on how to approach #4 as well as #6 to be solved.
Some Inspiration from [`tf.image.resize_image_with_crop_or_pad`][1]. [1]: https://github.com/tensorflow/tensorflow/blob/r1.3/tensorflow/python/ops/image_ops_impl.py#L572
Basically relates to * [ ] `mrtoct.data.transform.CenterPad` * [ ] `mrtoct.data.transform.CenterCrop` * [ ] `mrtoct.data.transform.CenterExctractSlice` * [ ] `mrtoct.data.transform.CenterExctractPatch` to check if * [ ] dimensions of input and target...