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Merge pull request #7 from andreped/dev
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Added support for brain extraction and different brain tumor tasks
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@andreped
andreped committed Jun 6, 2023
2 parents a949326 + 9351126 commit 52b4034
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Showing 6 changed files with 104 additions and 47 deletions.
1 change: 1 addition & 0 deletions .dockerignore
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Expand Up @@ -6,6 +6,7 @@ resources/
*.nii.gz
*.nrrd
*.obj
*.zip
*log.csv
*.ini
gradio_cached_examples/
1 change: 1 addition & 0 deletions .gitignore
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Expand Up @@ -6,6 +6,7 @@ resources/
*.nii.gz
*.nrrd
*.obj
*.zip
*log.csv
*.ini
gradio_cached_examples/
12 changes: 11 additions & 1 deletion Dockerfile
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Expand Up @@ -62,9 +62,19 @@ COPY --chown=user . $HOME/app
# Download pretrained parenchyma model
RUN wget "https://github.com/raidionics/Raidionics-models/releases/download/1.2.0/Raidionics-MRI_Meningioma-ONNX-v12.zip" && \
unzip "Raidionics-MRI_Meningioma-ONNX-v12.zip" && mkdir -p resources/models/ && mv MRI_Meningioma/ resources/models/MRI_Meningioma/
RUN wget "https://github.com/raidionics/Raidionics-models/releases/download/1.2.0/Raidionics-MRI_LGGlioma-ONNX-v12.zip" && \
unzip "Raidionics-MRI_LGGlioma-ONNX-v12.zip" && mv MRI_LGGlioma/ resources/models/MRI_LGGlioma/
RUN wget "https://github.com/raidionics/Raidionics-models/releases/download/1.2.0/Raidionics-MRI_Metastasis-ONNX-v12.zip" && \
unzip "Raidionics-MRI_Metastasis-ONNX-v12.zip" && mv MRI_Metastasis/ resources/models/MRI_Metastasis/
RUN wget "https://github.com/raidionics/Raidionics-models/releases/download/1.2.0/Raidionics-MRI_GBM-ONNX-v12.zip" && \
unzip "Raidionics-MRI_GBM-ONNX-v12.zip" && mv MRI_GBM/ resources/models/MRI_GBM/
RUN wget "https://github.com/raidionics/Raidionics-models/releases/download/1.2.0/Raidionics-MRI_Brain-ONNX-v12.zip" && \
unzip "Raidionics-MRI_Brain-ONNX-v12.zip" && mv MRI_Brain/ resources/models/MRI_Brain/
RUN rm -r *.zip

# Download test sample
RUN wget "https://github.com/andreped/neukit/releases/download/test-data/RegLib_C01_2.nii"
RUN wget "https://github.com/andreped/neukit/releases/download/test-data/RegLib_C01_1.nii" && \
wget "https://github.com/andreped/neukit/releases/download/test-data/RegLib_C01_2.nii"

# Download test sample
RUN pip install gdown && gdown "https://drive.google.com/uc?id=1shjSrFjS4PHE5sTku30PZTLPZpGu24o3"
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55 changes: 46 additions & 9 deletions neukit/gui.py
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Expand Up @@ -4,7 +4,7 @@


class WebUI:
def __init__(self, model_name:str = None, class_name:str = "meningioma", cwd:str = "/home/user/app/"):
def __init__(self, model_name:str = None, cwd:str = "/home/user/app/"):
# global states
self.images = []
self.pred_images = []
Expand All @@ -13,9 +13,25 @@ def __init__(self, model_name:str = None, class_name:str = "meningioma", cwd:str
self.nb_slider_items = 150

self.model_name = model_name
self.class_name = class_name
self.cwd = cwd

self.class_name = "meningioma" # default - but can be updated based on which task is chosen from dropdown
self.class_names = {
"meningioma": "MRI_Meningioma",
"low-grade": "MRI_LGGlioma",
"metastasis": "MRI_Metastasis",
"high-grade": "MRI_GBM",
"brain": "MRI_Brain",
}

self.result_names = {
"meningioma": "Tumor",
"low-grade": "Tumor",
"metastasis": "Tumor",
"high-grade": "Tumor",
"brain": "Brain",
}

# define widgets not to be rendered immediantly, but later on
self.slider = gr.Slider(1, self.nb_slider_items, value=1, step=1, label="Which 2D slice to show")
self.volume_renderer = gr.Model3D(
Expand All @@ -24,6 +40,10 @@ def __init__(self, model_name:str = None, class_name:str = "meningioma", cwd:str
visible=True,
elem_id="model-3d",
).style(height=512)

def set_class_name(self, value):
print("Changed task to:", value)
self.class_name = value

def combine_ct_and_seg(self, img, pred):
return (img, [(pred, self.class_name)])
Expand All @@ -33,7 +53,7 @@ def upload_file(self, file):

def load_mesh(self, mesh_file_name):
path = mesh_file_name.name
run_model(path, model_path=self.cwd + "resources/models/")
run_model(path, model_path=self.cwd + "resources/models/", task=self.class_names[self.class_name], name=self.result_names[self.class_name])
nifti_to_glb("prediction.nii.gz")

self.images = load_ct_to_numpy(path)
Expand All @@ -56,25 +76,42 @@ def run(self):
height: 512px;
margin: auto;
}
#upload {
height: 120px;
}
"""
with gr.Blocks(css=css) as demo:

with gr.Row():
file_output = gr.File(
file_count="single"
).style(full_width=False, size="sm")

file_output = gr.File(file_count="single", elem_id="upload") # elem_id="upload"
file_output.upload(self.upload_file, file_output, file_output)

run_btn = gr.Button("Run analysis").style(full_width=False, size="sm")
# with gr.Column():

model_selector = gr.Dropdown(
list(self.class_names.keys()),
label="Task",
info="Which task to perform - one model for each brain tumor type and brain extraction",
multiselect=False,
size="sm",
)
model_selector.input(
fn=lambda x: self.set_class_name(x),
inputs=model_selector,
outputs=None,
)

run_btn = gr.Button("Run analysis").style(full_width=False, size="lg")
run_btn.click(
fn=lambda x: self.load_mesh(x),
inputs=file_output,
outputs=self.volume_renderer
outputs=self.volume_renderer,
)

with gr.Row():
gr.Examples(
examples=[self.cwd + "RegLib_C01_2.nii"],
examples=[self.cwd + "RegLib_C01_1.nii", self.cwd + "RegLib_C01_2.nii"],
inputs=file_output,
outputs=file_output,
fn=self.upload_file,
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79 changes: 43 additions & 36 deletions neukit/inference.py
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Expand Up @@ -5,7 +5,7 @@
import traceback


def run_model(input_path: str, model_path: str, verbose: str = "info", task: str = "MRI_Meningioma"):
def run_model(input_path: str, model_path: str, verbose: str = "info", task: str = "MRI_Meningioma", name: str = "Tumor"):
logging.basicConfig()
logging.getLogger().setLevel(logging.WARNING)

Expand All @@ -17,49 +17,56 @@ def run_model(input_path: str, model_path: str, verbose: str = "info", task: str
logging.getLogger().setLevel(logging.ERROR)
else:
raise ValueError("Unsupported verbose value provided:", verbose)

# create sequence folder, rename patient, and add to temporary patient directory
filename = input_path.split("/")[-1]
splits = filename.split(".")
extension = ".".join(splits[1:])
patient_directory = "./patient/"
os.makedirs(patient_directory + "T0/", exist_ok=True)
shutil.copy(input_path, patient_directory + "T0/" + splits[0] + "-t1gd." + extension)

# define output directory to save results
output_path = "./result/prediction-" + splits[0] + "/"
os.makedirs(output_path, exist_ok=True)
# delete patient/result folder if they exist
if os.path.exists("./patient/"):
shutil.rmtree("./patient/")
if os.path.exists("./result/"):
shutil.rmtree("./result/")

try:
# create sequence folder, rename patient, and add to temporary patient directory
filename = input_path.split("/")[-1]
splits = filename.split(".")
extension = ".".join(splits[1:])
patient_directory = "./patient/"
os.makedirs(patient_directory + "T0/", exist_ok=True)
shutil.copy(input_path, patient_directory + "T0/" + splits[0] + "-t1gd." + extension)

# define output directory to save results
output_path = "./result/prediction-" + splits[0] + "/"
os.makedirs(output_path, exist_ok=True)

# Setting up the configuration file
rads_config = configparser.ConfigParser()
rads_config.add_section('Default')
rads_config.set('Default', 'task', 'neuro_diagnosis')
rads_config.set('Default', 'caller', '')
rads_config.add_section('System')
rads_config.set('System', 'gpu_id', "-1")
rads_config.set('System', 'input_folder', patient_directory)
rads_config.set('System', 'output_folder', output_path)
rads_config.set('System', 'model_folder', model_path)
rads_config.set('System', 'pipeline_filename', os.path.join(model_path, task, 'pipeline.json'))
rads_config.add_section('Runtime')
rads_config.set('Runtime', 'reconstruction_method', 'thresholding') # thresholding, probabilities
rads_config.set('Runtime', 'reconstruction_order', 'resample_first')
rads_config.set('Runtime', 'use_preprocessed_data', 'False')
# Setting up the configuration file
rads_config = configparser.ConfigParser()
rads_config.add_section('Default')
rads_config.set('Default', 'task', 'neuro_diagnosis')
rads_config.set('Default', 'caller', '')
rads_config.add_section('System')
rads_config.set('System', 'gpu_id', "-1")
rads_config.set('System', 'input_folder', patient_directory)
rads_config.set('System', 'output_folder', output_path)
rads_config.set('System', 'model_folder', model_path)
rads_config.set('System', 'pipeline_filename', os.path.join(model_path, task, 'pipeline.json'))
rads_config.add_section('Runtime')
rads_config.set('Runtime', 'reconstruction_method', 'thresholding') # thresholding, probabilities
rads_config.set('Runtime', 'reconstruction_order', 'resample_first')
rads_config.set('Runtime', 'use_preprocessed_data', 'False')

with open("rads_config.ini", "w") as f:
rads_config.write(f)
with open("rads_config.ini", "w") as f:
rads_config.write(f)

# finally, run inference
from raidionicsrads.compute import run_rads
# finally, run inference
from raidionicsrads.compute import run_rads

try:
run_rads(config_filename='rads_config.ini')

# rename and move final result
os.rename("./result/prediction-" + splits[0] + "/T0/" + splits[0] + "-t1gd_annotation-" + name + ".nii.gz", "./prediction.nii.gz")

except Exception as e:
print(e)

# rename and move final result
os.rename("./result/prediction-" + splits[0] + "/T0/" + splits[0] + "-t1gd_annotation-Tumor.nii.gz", "./prediction.nii.gz")


# Clean-up
if os.path.exists(patient_directory):
shutil.rmtree(patient_directory)
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3 changes: 2 additions & 1 deletion requirements.txt
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@@ -1,3 +1,4 @@
raidionicsrads @ https://github.com/dbouget/raidionics_rads_lib/releases/download/v1.1.0/raidionicsrads-1.1.0-py3-none-manylinux1_x86_64.whl
#raidionicsrads @ https://github.com/dbouget/raidionics_rads_lib/releases/download/v1.1.0/raidionicsrads-1.1.0-py3-none-manylinux1_x86_64.whl
#raidionicsrads @ https://github.com/dbouget/raidionics_rads_lib/releases/download/v1.1.0/raidionicsrads-1.1.0-py3-none-macosx_10_15_x86_64.whl
raidionicsrads @ git+https://github.com/andreped/raidionics_rads_lib
gradio==3.32.0

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