Airways segmentation running locally on a resampled version of the test CT[skip ci]

AeroPath/gui.py

@@ -0,0 +1,171 @@
+import os
+
+import gradio as gr
+
+from .inference import run_model
+from .utils import load_ct_to_numpy
+from .utils import load_pred_volume_to_numpy
+from .utils import nifti_to_glb
+
+
+class WebUI:
+    def __init__(
+        self,
+        model_name: str = None,
+        cwd: str = "/home/user/app/",
+        share: int = 1,
+    ):
+        # global states
+        self.images = []
+        self.pred_images = []
+
+        # @TODO: This should be dynamically set based on chosen volume size
+        self.nb_slider_items = 415
+
+        self.model_name = model_name
+        self.cwd = cwd
+        self.share = share
+
+        self.class_name = "airways"  # default
+        self.class_names = {
+            "airways": "CT_Airways",
+            "lungs": "CT_Lungs",
+        }
+
+        self.result_names = {
+            "airways": "Airways",
+            "lungs": "Lungs",
+        }
+
+        # define widgets not to be rendered immediately, 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(
+            clear_color=[0.0, 0.0, 0.0, 0.0],
+            label="3D Model",
+            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)])
+
+    def upload_file(self, file):
+        return file.name
+
+    def process(self, mesh_file_name):
+        path = mesh_file_name.name
+        run_model(
+            path,
+            model_path=os.path.join(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)
+        self.pred_images = load_pred_volume_to_numpy("./prediction.nii.gz")
+        return "./prediction.obj"
+
+    def get_img_pred_pair(self, k):
+        k = int(k) - 1
+        out = [gr.AnnotatedImage.update(visible=False)] * self.nb_slider_items
+        out[k] = gr.AnnotatedImage.update(
+            self.combine_ct_and_seg(self.images[k], self.pred_images[k]),
+            visible=True,
+        )
+        return out
+
+    def run(self):
+        css = """
+        #model-3d {
+        height: 512px;
+        }
+        #model-2d {
+        height: 512px;
+        margin: auto;
+        }
+        #upload {
+        height: 120px;
+        }
+        """
+        with gr.Blocks(css=css) as demo:
+            with gr.Row():
+                file_output = gr.File(file_count="single", elem_id="upload")
+                file_output.upload(self.upload_file, file_output, file_output)
+
+                model_selector = gr.Dropdown(
+                    list(self.class_names.keys()),
+                    label="Task",
+                    info="Which task to perform - one model for"
+                    "airways and lungs 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.process(x),
+                    inputs=file_output,
+                    outputs=self.volume_renderer,
+                )
+
+            with gr.Row():
+                gr.Examples(
+                    examples=[
+                        os.path.join(self.cwd, "test_thorax_CT_ds.nii"),
+                        os.path.join(self.cwd, "test_thorax_CT_ds.nii"),
+                    ],
+                    inputs=file_output,
+                    outputs=file_output,
+                    fn=self.upload_file,
+                    cache_examples=True,
+                )
+
+            with gr.Row():
+                with gr.Box():
+                    with gr.Column():
+                        image_boxes = []
+                        for i in range(self.nb_slider_items):
+                            visibility = True if i == 1 else False
+                            t = gr.AnnotatedImage(
+                                visible=visibility, elem_id="model-2d"
+                            ).style(
+                                color_map={self.class_name: "#ffae00"},
+                                height=512,
+                                width=512,
+                            )
+                            image_boxes.append(t)
+
+                        self.slider.input(
+                            self.get_img_pred_pair, self.slider, image_boxes
+                        )
+
+                        self.slider.render()
+
+                with gr.Box():
+                    self.volume_renderer.render()
+
+        # sharing app publicly -> share=True:
+        # https://gradio.app/sharing-your-app/
+        # inference times > 60 seconds -> need queue():
+        # https://github.com/tloen/alpaca-lora/issues/60#issuecomment-1510006062
+        demo.queue().launch(
+            server_name="0.0.0.0", server_port=7860, share=self.share
+        )

AeroPath/inference.py

@@ -0,0 +1,103 @@
+import configparser
+import logging
+import os
+import shutil
+import traceback
+
+
+def run_model(
+    input_path: str,
+    model_path: str,
+    verbose: str = "info",
+    task: str = "CT_Airways",
+    name: str = "Airways",
+):
+    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)
+
+    # delete patient/result folder if they exist
+    if os.path.exists("./patient/"):
+        shutil.rmtree("./patient/")
+    if os.path.exists("./result/"):
+        shutil.rmtree("./result/")
+
+    patient_directory = ''
+    output_path = ''
+    try:
+        # setup 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", "mediastinum_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
+
+        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",
+        )
+        # Clean-up
+        if os.path.exists(patient_directory):
+            shutil.rmtree(patient_directory)
+        if os.path.exists(output_path):
+            shutil.rmtree(output_path)
+    except Exception as e:
+        print(traceback.format_exc())
+        # Clean-up
+        if os.path.exists(patient_directory):
+            shutil.rmtree(patient_directory)
+        if os.path.exists(output_path):
+            shutil.rmtree(output_path)

Dockerfile

@@ -61,6 +61,7 @@ RUN wget "https://github.com/raidionics/Raidionics-models/releases/download/1.2.
 RUN rm -r *.zip
 
 # Download test sample
+# @TODO: I have resampled the volume to 1mm isotropic for faster computation
 RUN wget "https://github.com/andreped/neukit/releases/download/test-data/test_thorax_CT.nii.gz"
 
 # CMD ["/bin/bash"]

README.md

@@ -51,12 +51,12 @@ It is also possible to run the app locally without Docker. Just setup a virtual
 Note that the current working directory would need to be adjusted based on where `AeroPath` is located on disk.
 
 ```
-git clone https://github.com/andreped/AeroPath.git
+git clone https://github.com/raidionics/AeroPath.git
 cd AeroPath/
 
-virtualenv -ppython3 venv --clear
+virtualenv -python3 venv --clear
 source venv/bin/activate
-pip install -r requirements.txt
+pip install -r ./demo/requirements.txt
 
 python app.py --cwd ./
 ```

app.py

@@ -0,0 +1,41 @@
+import os
+from argparse import ArgumentParser
+
+from AeroPath.gui import WebUI
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument(
+        "--cwd",
+        type=str,
+        default="/home/user/app/",
+        help="Set current working directory (path to app.py).",
+    )
+    parser.add_argument(
+        "--share",
+        type=int,
+        default=1,
+        help="Whether to enable the app to be accessible online"
+        "-> setups a public link which requires internet access.",
+    )
+    args = parser.parse_args()
+
+    print("Current working directory:", args.cwd)
+
+    if not os.path.exists(args.cwd):
+        raise ValueError("Chosen 'cwd' is not a valid path!")
+    if args.share not in [0, 1]:
+        raise ValueError(
+            "The 'share' argument can only be set to 0 or 1, but was:",
+            args.share,
+        )
+
+    # initialize and run app
+    print("Launching demo...")
+    app = WebUI(cwd=args.cwd, share=args.share)
+    app.run()
+
+
+if __name__ == "__main__":
+    main()

demo/requirements.txt

@@ -1,3 +1,3 @@
-raidionicsrads@git+https://github.com/andreped/raidionics_rads_lib.git
-gradio==3.44.4
-pandas==2.0.0
+raidionicsrads@git+https://github.com/dbouget/raidionics_rads_lib
+gradio==3.36.1
+gradio_client==0.2.7