Working prototype - to be refactored

.dockerignore

@@ -1 +1,10 @@
 venv/
+*__pycache__/
+resources/
+*.DS_Store
+*.nii
+*.nii.gz
+*.nrrd
+*.obj
+*log.csv
+*.ini

.gitignore

@@ -1 +1,10 @@
 venv/
+*__pycache__/
+resources/
+*.DS_Store
+*.nii
+*.nii.gz
+*.nrrd
+*.obj
+*log.csv
+*.ini

app.py

@@ -4,14 +4,11 @@ from neukit.gui import WebUI
 def main():
     print("Launching demo...")
 
-    # cwd = "/Users/andreped/workspace/livermask/"  # local testing -> macOS
-    cwd = "/home/user/app/"  # production -> docker
-
-    model_name = "model.h5"  # assumed to lie in `cwd` directory
-    class_name = "parenchyma"
+    cwd = "/Users/andreped/workspace/neukit/"  # local testing -> macOS
+    # cwd = "/home/user/app/"  # production -> docker
 
     # initialize and run app
-    app = WebUI(model_name=model_name, class_name=class_name, cwd=cwd)
+    app = WebUI(cwd=cwd)
     app.run()
 
 

neukit/gui.py

@@ -1,17 +1,16 @@
 import gradio as gr
-from .utils import load_ct_to_numpy, load_pred_volume_to_numpy
-from .compute import run_model
-from .convert import nifti_to_glb
+from .utils import load_ct_to_numpy, load_pred_volume_to_numpy, nifti_to_glb
+from .inference import run_model
 
 
 class WebUI:
-    def __init__(self, model_name:str = None, class_name:str = None, cwd:str = None):
+    def __init__(self, model_name:str = None, class_name:str = "meningioma", cwd:str = "/home/user/app/"):
         # global states
         self.images = []
         self.pred_images = []
 
         # @TODO: This should be dynamically set based on chosen volume size
-        self.nb_slider_items = 100
+        self.nb_slider_items = 150
 
         self.model_name = model_name
         self.class_name = class_name
@@ -31,12 +30,19 @@ class WebUI:
     def upload_file(self, file):
         return file.name
     
-    def load_mesh(self, mesh_file_name, model_name):
+    def get_file_extension(self, file):
+        filename = file.split("/")[-1]
+        splits = filename.split(".")
+        return ".".join(splits[1:])
+    
+    def load_mesh(self, mesh_file_name):
         path = mesh_file_name.name
-        run_model(path, model_name)
-        nifti_to_glb("prediction-livermask.nii")
+        extension = self.get_file_extension(path)
+        #run_model(path, model_path=self.cwd + "resources/models/")
+        #nifti_to_glb("prediction." + extension)
+
         self.images = load_ct_to_numpy(path)
-        self.pred_images = load_pred_volume_to_numpy("./prediction-livermask.nii")
+        self.pred_images = load_pred_volume_to_numpy("./prediction." + extension)
         self.slider = self.slider.update(value=2)
         return "./prediction.obj"
     
@@ -51,26 +57,26 @@ class WebUI:
 
             with gr.Row().style(equal_height=True):
                 file_output = gr.File(
-                    file_types=[".nii", ".nii.nz"],
+                    #file_types=[".nii", ".nii.gz"],
                     file_count="single"
                 ).style(full_width=False, size="sm")
                 file_output.upload(self.upload_file, file_output, file_output)
 
                 run_btn = gr.Button("Run analysis").style(full_width=False, size="sm")
                 run_btn.click(
-                    fn=lambda x: self.load_mesh(x, model_name=self.cwd + self.model_name),
+                    fn=lambda x: self.load_mesh(x),
                     inputs=file_output,
                     outputs=self.volume_renderer
                 )
             
-            with gr.Row().style(equal_height=True):
-                gr.Examples(
-                    examples=[self.cwd + "test-volume.nii"],
-                    inputs=file_output,
-                    outputs=file_output,
-                    fn=self.upload_file,
-                    cache_examples=True,
-                )
+            #with gr.Row().style(equal_height=True):
+                #gr.Examples(
+                #    examples=[self.cwd + "test-volume.nii"],
+                #    inputs=file_output,
+                #    outputs=file_output,
+                #    fn=self.upload_file,
+                #    cache_examples=True,
+                #)
             
             with gr.Row().style(equal_height=True):
                 with gr.Box():

neukit/inference.py

@@ -0,0 +1,73 @@
+import os
+import shutil
+import configparser
+import logging
+import traceback
+
+
+def run_model(input_path: str, model_path: str, verbose: str = "info", task: str = "MRI_Meningioma"):
+    logging.basicConfig()
+    logging.getLogger().setLevel(logging.WARNING)
+
+    if verbose == 'debug':
+        logging.getLogger().setLevel(logging.DEBUG)
+    elif verbose == 'info':
+        logging.getLogger().setLevel(logging.INFO)
+    elif verbose == 'error':
+        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)
+    os.rename(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)
+
+    print("orig input:", input_path)
+    print("updated input:", patient_directory + "T0/" + splits[0] + "-t1gd." + extension)
+    print("patient_dir:", patient_directory)
+    print("output path:", output_path)
+    print("model path:", model_path)
+
+    # 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)
+
+    # finally, run inference
+    from raidionicsrads.compute import run_rads
+
+    try:
+        run_rads(config_filename='rads_config.ini')
+    except Exception as e:
+        print(e)
+    
+    # rename and move final result
+    os.rename("./result/prediction-" + splits[0] + "/T0/" + splits[0] + "-t1gd_annotation-Tumor." + extension, "./prediction." + extension)
+    
+    # Clean-up
+    if os.path.exists(patient_directory):
+        shutil.rmtree(patient_directory)
+    if os.path.exists(output_path):
+        shutil.rmtree(output_path)

requirements.txt

@@ -1,3 +1,5 @@
-raidionicsrads @ https://github.com/dbouget/raidionics_rads_lib/releases/download/v1.1.0/raidionicsrads-1.1.0-py3-none-manylinux1_x86_64.whl
-onnxruntime-gpu==1.12.1
+#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
+#onnxruntime-gpu==1.12.1
+#onnxruntime==1.12.1
 gradio==3.32.0