Merge branch 'revising-config' into 'master'

Revising config

See merge request !7

anisotropic_nets_brats_challenge_model_zoo.md

@@ -13,12 +13,13 @@ to the online validation leaderboard of [BRATS challenge 2017](https://www.cbica
 
 ## Downloading model zoo files
 
-If you cloned the NiftyNet repository,
-the network weights and examples data can be downloaded with the command
+The network weights and examples data can be downloaded with the command
 ```bash
-python net_download.py anisotropic_nets_brats_challenge_model_zoo
+net_download anisotropic_nets_brats_challenge_model_zoo
 ```
 
+(Replace `net_download` with `python net_download.py` if you cloned the NiftyNet repository.)
+
 ## Generating segmentations for example data
 
 Generate segmentations for the included example image with the command,
@@ -39,7 +40,7 @@ net_run inference -a anisotropic_nets_brats_challenge.brats_seg_app.BRATSApp \
                   -c ~/niftynet/extensions/anisotropic_nets_brats_challenge/whole_tumor_sagittal.ini
 ```
 
-Replace `net_run` with `python net_run.py` if you cloned the NiftyNet repository.
+(Replace `net_run` with `python net_run.py` if you cloned the NiftyNet repository.)
 
 ## Generating averaged volume from the outcomes of the previous step
 

dense_vnet_abdominal_ct_model_zoo.md

 # Automatic multi-organ segmentation on abdominal CT with dense v-networks
 
-This page describes how to acquire and use the network described in 
+This page describes how to acquire and use the network described in
 
 Eli Gibson, Francesco Giganti, Yipeng Hu, Ester Bonmati, Steve
 Bandula, Kurinchi Gurusamy, Brian Davidson, Stephen P. Pereira,
@@ -13,31 +13,34 @@ nearby organs (liver, gallbladder, spleen, left kidney).
 
 ## Downloading model zoo files
 
-If you cloned the NiftyNet repository, the network weights and examples data can be downloaded with the command
+The network weights and examples data can be downloaded with the command
 ```bash
-python net_download.py dense_vnet_abdominal_ct_model_zoo
+net_download dense_vnet_abdominal_ct_model_zoo
 ```
 
+(Replace `net_download` with `python net_download.py` if you cloned the NiftyNet repository.)
 
-Alternatively, you can download the 
-[model zoo entry](https://www.dropbox.com/s/yddopkblhe7gfsj/dense_vnet_abdominal_ct_model_zoo.tar.gz?dl=1) 
-and the [example data](https://www.dropbox.com/s/5fk0m9v12if5da9/dense_vnet_abdominal_ct_model_zoo_data.tar.gz?dl=1) manually. 
-Unzip `dense_vnet_abdominal_ct_model_zoo.tar.gz` into 
-`~/niftynet/models/dense_vnet_abdominal_ct_model_zoo/` and 
-`dense_vnet_abdominal_ct_model_zoo_data.tar.gz` into 
-`~/niftynet/data/dense_vnet_abdominal_ct_model_zoo_data/`.
 
-Make sure that the model directory (`~/niftynet/models/` by default) is on the `PYTHONPATH`.
+Alternatively, you can manually download:
+- [model zoo code](https://www.dropbox.com/s/ptu46os7lfmj0dl/dense_vnet_abdominal_ct_code_config.tar.gz?dl=1)
+- [trained network weights](https://www.dropbox.com/s/zvc8stqo6womvou/dense_vnet_abdominal_ct_weights.tar.gz?dl=1)
+- [example data](https://www.dropbox.com/s/5fk0m9v12if5da9/dense_vnet_abdominal_ct_model_zoo_data.tar.gz?dl=1)
+
+And unzip:
+- `dense_vnet_abdominal_ct_code_config.tar.gz.tar.gz` into `~/niftynet/extensions/dense_vnet_abdominal_ct/`
+- `dense_vnet_abdominal_ct_weights.tar.gz` into `~/niftynet/models/dense_vnet_abdominal_ct/`
+- `dense_vnet_abdominal_ct_model_zoo_data.tar.gz` into `~/niftynet/data/dense_vnet_abdominal_ct/`
+
+Make sure that the model directory (`~/niftynet/extensions/` by default) is on the `PYTHONPATH`.
 
 ## Generating segmentations for example data
 
-Generate segmentations for the included example image with the command 
+Generate segmentations for the included example image with the command
 ```bash
 net_segment inference -c ~/niftynet/extensions/dense_vnet_abdominal_ct/config.ini
 ```
-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
 
@@ -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).
+
+
+

highres3dnet_brain_parcellation_model_zoo.md

@@ -3,27 +3,26 @@
 This page describes how to acquire and use the network described in:
 
 Li W., Wang G., Fidon L., Ourselin S., Cardoso M.J., Vercauteren T. (2017)
-On the Compactness, Efficiency, and Representation of 3D 
+On the Compactness, Efficiency, and Representation of 3D
 Convolutional Networks: Brain Parcellation as a Pretext Task.
 In: Information Processing in Medical Imaging. IPMI 2017
 
-This network parcellates 160 types of structures 
+This network parcellates 160 types of structures
 (including 155 neuroanatomical structures) from brain MR images.
 ## Downloading model zoo files
 
-If you cloned the NiftyNet repository, 
-the network weights and examples data can be downloaded with the command
+The network weights and examples data can be downloaded with the command
 ```bash
-python net_download.py highres3dnet_brain_parcellation_model_zoo
+net_download highres3dnet_brain_parcellation_model_zoo
 ```
 
+(Replace `net_download` with `python net_download.py` if you cloned the NiftyNet repository.)
+
 ## Generating segmentations for example data
 
-Generate segmentations for the included example image with the command 
+Generate segmentations for the included example image with the command
 ```bash
 net_segment inference -c ~/niftynet/extensions/highres3dnet_brain_parcellation/highres3dnet_config_eval.ini
 ```
 
-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.
\ No newline at end of file
+Replace `net_segment` with `python net_segment.py` if you cloned the NiftyNet repository.

ultrasound_simulator_gan_model_zoo.md

 # Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks
 
-This page describes how to acquire and use the network described in 
+This page describes how to acquire and use the network described in
 
 Yipeng Hu, Eli Gibson, Li-Lin Lee, Weidi Xie, Dean C. Barratt, Tom Vercauteren, J. Alison Noble
 (2017). [Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks](https://arxiv.org/abs/1707.05392), In MICCAI RAMBO 2017
@@ -9,27 +9,32 @@ Yipeng Hu, Eli Gibson, Li-Lin Lee, Weidi Xie, Dean C. Barratt, Tom Vercauteren,
 
 If you cloned the NiftyNet repository, the network weights and examples data can be downloaded with the command
 ```bash
-python net_download.py ultrasound_simulator_gan_model_zoo
-``` 
+net_download ultrasound_simulator_gan_model_zoo
+```
+(Replace `net_download` with `python net_download.py` if you cloned the NiftyNet repository.)
+
+Alternatively, you can manually download:
+- [model zoo entry](https://www.dropbox.com/s/089l4ixd2k7fiyy/ultrasound_simulator_gan_code.tar.gz?dl=1)
+- [example data](https://www.dropbox.com/s/hu1i8yjdptwq6wj/ultrasound_simulator_gan_model_zoo_data.tar.gz?dl=1)
+- [trained network weights](https://www.dropbox.com/s/95smm4i464nwczm/ultrasound_simulator_gan_weights.tar.gz?dl=1)
 
-If you install NiftyNet via pip, you won't have the net_download feature yet. You can download the 
-[model zoo entry](https://www.dropbox.com/s/yddopkblhe7gfsj/dense_vnet_abdominal_ct_model_zoo.tar.gz?dl=1) 
-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 
-~/niftynet/data/ultrasound_simulator_gan_model_zoo_data/.
+and unzip:
+- `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
 ```bash
 net_gan inference -c ~/niftynet/extensions/ultrasound_simulator_gan/config.ini
 ```
 
-Replace `net_segment` with `python net_gan.py` if you cloned the NiftyNet repository. 
+Replace `net_segment` with `python net_gan.py` if you cloned the NiftyNet repository.
 
 Replace `~/niftynet/` if you specified a custom download path in the `net_download` command.