Commit 92943c09 authored by Wenqi Li's avatar Wenqi Li

amend net_download commands

parent 5270d9a4
......@@ -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
......
# 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
......
......@@ -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.
# 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.
......
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