Replace `net_segment` with `python net_segment.py` if you cloned the NiftyNet repository.
Replace `net_segment` with `python net_segment.py` if you cloned the NiftyNet repository.
Replace `~/niftynet/` if you specified a custom download path in the `net_download` command.
## Generating segmentations for your own data
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@@ -57,3 +60,12 @@ You may need to change the `path_to_search` and `filename_contains` lines in the
Generate segmentations with the command `net_segment inference -c edited_config.ini`, replacing `edited_config.ini` with the path to the new configuration file. Segmentations will be saved in the path specified by the `save_seg_dir` setting with names corresponding to your input file names, with a `_niftynet_out.nii.gz` suffix.
Please Note:
* To achieve an efficient parcellation, a GPU with at least 10GB memory is required.
* Please change the environment variable `CUDA_VISIBLE_DEVICES` to an appropriate value if necessary (e.g., `export CUDA_VISIBLE_DEVICES=0` will allow NiftyNet to use the `0`-th GPU).
and the [example data](https://www.dropbox.com/s/5fk0m9v12if5da9/dense_vnet_abdominal_ct_model_zoo_data.tar.gz?dl=1) manually.
Unzip ultrasound_simulator_gan_model_zoo.tar.gz into ~/niftynet/models/ultrasound_simulator_gan_model_zoo/ and ultrasound_simulator_gan_model_zoo_data.tar.gz into
-`ultrasound_simulator_gan_code.tar.gz` into `~/niftynet/extensions/ultrasound_simulator_gan/`
-`ultrasound_simulator_gan_model_zoo_data.tar.gz` into `~/niftynet/data/ultrasound_simulator_gan/`
-`ultrasound_simulator_gan_weights.tar.gz` into `~/niftynet/models/ultrasound_simulator_gan/`
Make sure that the model directory (`~/niftynet/models/` by default) is on the PYTHONPATH.
Make sure that the model directory (`~/niftynet/extensions/` by default) is on the PYTHONPATH.
This network generates ultrasound images conditioned by a coordinate map. Some example coordinate maps are included in the model zoo data. Additional examples are available [here](https://www.dropbox.com/s/w0frdlxaie3mndg/test_data.tar.gz?dl=0)).
## Generating segmentations for example data
Generate segmentations for the included example conditioning data with the command
Generate segmentations for the included example conditioning data with the command