diff --git a/hd_gliomouse/__init__.py b/hd_gliomouse/__init__.py
index 74b37f7..d8dbfb1 100644
--- a/hd_gliomouse/__init__.py
+++ b/hd_gliomouse/__init__.py
@@ -1,10 +1,10 @@
from __future__ import absolute_import
from . import utils
from . import paths
from . import setup_hd_gliomouse
print("\n########################")
print("If you are using hd-bet, please cite the following papers:\n")
-print("UPCOMMING\n")
-print("Isensee, Fabian, et al. \"nnU-Net: Breaking the Spell on Successful Medical Image Segmentation.\" "
- "arXiv preprint arXiv:1904.08128 (2019). (https://arxiv.org/abs/1904.08128)\n")
+print("UPCOMING\n")
+print("Isensee, Fabian, et al. \"Automated Design of Deep Learning Methods for Biomedical Image Segmentation.\" "
+ "arXiv preprint arXiv:1904.08128 (2020). (https://arxiv.org/abs/1904.08128)\n")
diff --git a/hd_gliomouse/hd_gliomouse_predict.py b/hd_gliomouse/hd_gliomouse_predict.py
index 90dbdfc..7827089 100644
--- a/hd_gliomouse/hd_gliomouse_predict.py
+++ b/hd_gliomouse/hd_gliomouse_predict.py
@@ -1,55 +1,47 @@
# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from hd_gliomouse.utils import blockPrint, enablePrint
blockPrint()
from nnunet.inference.predict import predict_cases
enablePrint()
import argparse
from hd_gliomouse.paths import folder_with_parameter_files
from hd_gliomouse.setup_hd_gliomouse import maybe_download_weights
def main():
- parser = argparse.ArgumentParser(description="This script will allow you to predict a single case with hd_glio. "
- "If you have multiple cases, please use hd_glio_predict_folder (this one "
+ parser = argparse.ArgumentParser(description="This script will allow you to predict a single case with hd_gliomouse. "
+ "If you have multiple cases, please use hd_gliomouse_predict_folder (this one "
"will be substantially faster for multiple cases because we can "
- "interleave preprocessing, GPU prediction and nifti export."
- "\n"
- "IMPORTANT!\n"
- "The input files must be brain extracted with the non-brain region being "
- "0 (you can achieve that by using hd-bet "
- "(https://github.com/MIC-DKFZ/HD-BET). Furthermore, the input files "
- "must be co-registered and in the same co-ordinate system (pixels "
- "arrays must be aligned)\n"
- "All input files must be niftis (.nii.gz)")
+ "interleave preprocessing, GPU prediction and nifti export.")
parser.add_argument("-i", type=str, required=True,
help="input file")
parser.add_argument("-o", "--output_file", type=str, required=True,
help="output filename. Must end with .nii.gz")
args = parser.parse_args()
inp = args.i
output_file = args.output_file
maybe_download_weights()
predict_cases(folder_with_parameter_files, [[inp, ]], [output_file], (0, 1, 2, 3, 4), False, 1, 1, None, True,
None, True)
if __name__ == "__main__":
main()
diff --git a/hd_gliomouse/hd_gliomouse_predict_folder.py b/hd_gliomouse/hd_gliomouse_predict_folder.py
index a7dffd3..2760220 100644
--- a/hd_gliomouse/hd_gliomouse_predict_folder.py
+++ b/hd_gliomouse/hd_gliomouse_predict_folder.py
@@ -1,60 +1,60 @@
# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from batchgenerators.utilities.file_and_folder_operations import subfiles, join
from hd_gliomouse.utils import blockPrint, enablePrint
blockPrint()
from nnunet.inference.predict import predict_cases
enablePrint()
import argparse
from hd_gliomouse.paths import folder_with_parameter_files
from hd_gliomouse.setup_hd_gliomouse import maybe_download_weights
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input_folder", type=str, required=True,
help="folder with input files. All .nii.gz files in this folder will be processed.")
parser.add_argument("-o", "--output_folder", type=str, required=True,
help="output folder. This is there the resulting segmentations will be saved. Cannot be the "
"same folder as the input folder. If output_folder does not exist "
"it will be created")
parser.add_argument("-p", "--processes", default=4, type=str, required=False,
help="number of processes for data preprocessing and nifti export. You should not have to "
"touch this. So don't unless there is a clear indication that it is required. Default: 4")
- parser.add_argument('--overwrite_existing', default=True, type=str, required=False,
+ parser.add_argument('--keep_existing', default=True, type=str, required=False, action='store_false',
help="set to False to keep segmentations in output_folder and continue where you left off "
- "(useful if something crashes). If True then all segmentations that may already be "
- "present in output_folder will be overwritten. Default: True")
+ "(useful if something crashes). If this flag is not set, all segmentations that may "
+ "already be present in output_folder will be overwritten.")
args = parser.parse_args()
input_folder = args.input_folder
output_folder = args.output_folder
processes = args.processes
- overwrite_existing = args.overwrite_existing
+ keep_existing = args.keep_existing
maybe_download_weights()
# we must generate a list of input filenames
nii_files = subfiles(input_folder, suffix='.nii.gz', join=False)
input_list_of_lists = [[join(input_folder, i)] for i in nii_files]
output_filenames = [join(output_folder, i) for i in nii_files]
predict_cases(folder_with_parameter_files, input_list_of_lists, output_filenames, (0, 1, 2, 3, 4), False, processes,
- processes, None, True, None, overwrite_existing, False, 2, None, 3, 0)
+ processes, None, True, None, not keep_existing, False, 2, None, 3, 0)
if __name__ == "__main__":
main()
diff --git a/readme.md b/readme.md
new file mode 100644
index 0000000..3e1789d
--- /dev/null
+++ b/readme.md
@@ -0,0 +1,31 @@
+# Installation instructions
+## Installation requirements
+hd-gliomouse only runs on Linux. You need a pytorch capable GPU with 4GB of GPU memory to run hd-gliomouse.
+
+1) We strongly recommend you install hd-gliomouse in a separate python virtual environment. [Here is a quick how-to for Ubuntu.](https://linoxide.com/linux-how-to/setup-python-virtual-environment-ubuntu/)
+2) Once you set up the environment (and activated it), install hd-gliomouse with pip: `pip install hd_gliomouse` - Done
+
+This will install hd_gliomouse commands directly onto your system. You can use them from anywhere.
+
+# Usage
+
+## Run hd-gliomouse
+hd-gliomouse provides two main scripts: `hd_gliomouse_predict` and `hd_gliomouse_predict_folder`.
+
+### Predicting a single case
+`hd_gliomouse_predict` can be used to predict a single image. It is useful for exploration or if the number of cases
+to be procesed is low. Here is how to use it:
+
+`hd_gliomouse_predict -i INPUT_FILE -o OUTPUT_FILE`
+
+INPUT_FILE and OUTPUT_FILE must be a niftis (end with .nii.gz).
+
+### Predicting multiple cases
+`hd_gliomouse_predict_folder` is useful for batch processing, especially if the number of cases to be processed is large. By
+interleaving preprocessing, inference and segmentation export we can speed up the prediction significantly. Furthermore,
+the pipeline is initialized only once for all cases, again saving a lot of computation and I/O time. Here is how to use it:
+
+`hd_gliomouse_predict_folder -i INPUT_FOLDER -o OUTPUT_FOLDER`
+
+INPUT_FOLDER must contain nifti images (.nii.gz). The results will be written to the OUTPUT_FOLDER (with the same file names).
+ If the output folder does not exist it will be created.
\ No newline at end of file