Comments (3)
A brief overview of NiftyNet components in the published paper.
from niftynet.
The load_image
function is use in image_type.py
ultimately in the implementation of ImageFactory
. This is used eventually by ImageReader
in image_reader.py
which does the actual work of loading images and creating the image dictionary passed onto the network. In the application definitions ImageReader
is instantiated for each set of input and stored in self.readers
.
from niftynet.
Yes in the application you call the sampler and load the image using Sampler's "pop_batch_op", instead of feeding data in "sess.run(variables, feed_data_dict = data_dict)".
Although I haven't found how the image augmentation is implemented...
from niftynet.
Related Issues (20)
- Expected size of adding custom dataset for classification task on MR dataset?
- Provided indices are out-of-bounds w.r.t. dense side with broadcasted shape. Histogram error? HOT 1
- Using GANs without conditioning
- upgrade to support tensorflow 1.15
- If Niftynet supports Xavier initialization?
- Remove unconnected islands
- Default values for the bias field layer might be too large
- Float comparison in histogram normalisation layer
- Handle SimpleITK's lack of thread safety
- Sections containing `csv_data_file` are not loaded
- AdamW optimizer
- TensorRT frozen graphs - integration with NiftyNet
- Feature: Rewrite NiftyNet to use TensorFlow 2.0 HOT 1
- Using NiftyNet to do segmentation of MRI scans(in .nii extension) to grey matter,white matter and csf
- NiftiNet Roadmap HOT 2
- Niftynet window aggregator function for inferens - explaination needed
- using other DL framework
- How to train a model with one input and multi-output?
- dense-vnet to Monai HOT 1
- Segmentation classes description not available
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from niftynet.