Duplicate from katielink/brain_tumor_segmentation

Co-authored-by: Katie Link <[email protected]>

.gitattributes

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README.md

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+---
+title: Brain Tumor Segmentation
+emoji: 🧠
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 3.1.1
+app_file: app.py
+pinned: false
+license: other
+tags:
+- monai
+duplicated_from: katielink/brain_tumor_segmentation
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import gradio as gr
+import torch
+from monai import bundle
+from monai.transforms import (
+    Compose,
+    LoadImaged,
+    EnsureChannelFirstd,
+    Orientationd,
+    NormalizeIntensityd,
+    Activationsd,
+    AsDiscreted,
+    ScaleIntensityd,
+)
+
+# Define the bundle name and path for downloading
+BUNDLE_NAME = 'spleen_ct_segmentation_v0.1.0'
+BUNDLE_PATH = os.path.join(torch.hub.get_dir(), 'bundle', BUNDLE_NAME)
+
+# Title and description
+title = '<h1 style="text-align: center;">Segment Brain Tumors with MONAI! 🧠 </h1>'
+description = """
+## 🚀 To run
+
+Upload a brain MRI image file, or try out one of the examples below! 
+If you want to see a different slice, update the slider.
+
+More details on the model can be found [here!](https://huggingface.co/katielink/brats_mri_segmentation_v0.1.0)
+
+## ⚠️ Disclaimer
+
+This is an example, not to be used for diagnostic purposes.
+"""
+
+references = """
+## 👀 References
+
+1. Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.
+2. Menze BH, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694
+3. Bakas S, et al. "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI:10.1038/sdata.2017.117
+"""
+
+examples = [
+    ['examples/BRATS_485.nii.gz', 65],
+    ['examples/BRATS_486.nii.gz', 80]
+]
+
+# Load the MONAI pretrained model from Hugging Face Hub
+model, _, _ = bundle.load(
+    name = BUNDLE_NAME,
+    source = 'huggingface_hub',
+    repo = 'katielink/brats_mri_segmentation_v0.1.0',
+    load_ts_module=True,
+)
+
+# Use GPU if available
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# Load the parser from the MONAI bundle's inference config
+parser = bundle.load_bundle_config(BUNDLE_PATH, 'inference.json')
+
+# Compose the preprocessing transforms
+preproc_transforms = Compose(
+    [
+        LoadImaged(keys=["image"]),
+        EnsureChannelFirstd(keys="image"),
+        Orientationd(keys=["image"], axcodes="RAS"),
+        NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True),
+    ]
+)
+
+# Get the inferer from the bundle's inference config
+inferer = parser.get_parsed_content(
+    'inferer',
+    lazy=True, eval_expr=True, instantiate=True
+)
+
+# Compose the postprocessing transforms
+post_transforms = Compose(
+    [
+        Activationsd(keys='pred', sigmoid=True),
+        AsDiscreted(keys='pred', threshold=0.5),
+        ScaleIntensityd(keys='image', minv=0., maxv=1.)
+    ]
+)
+
+
+# Define the predict function for the demo
+def predict(input_file, z_axis, model=model, device=device):
+    # Load and process data in MONAI format
+    data = {'image': [input_file.name]}
+    data = preproc_transforms(data)
+    
+    # Run inference and post-process predicted labels
+    model.to(device)
+    model.eval()
+    with torch.no_grad():
+        inputs = data['image'].to(device)
+        data['pred'] = inferer(inputs=inputs[None,...], network=model)
+        data = post_transforms(data)
+    
+    # Convert tensors back to numpy arrays
+    data['image'] = data['image'].numpy()
+    data['pred'] = data['pred'].cpu().detach().numpy()
+    
+    # Magnetic resonance imaging sequences
+    t1c = data['image'][0, :, :, z_axis]  # T1-weighted, post contrast
+    t1 = data['image'][1, :, :, z_axis]  # T1-weighted, pre contrast
+    t2 = data['image'][2, :, :, z_axis]  # T2-weighted
+    flair = data['image'][3, :, :, z_axis]  # FLAIR
+    
+    # BraTS labels
+    tc = data['pred'][0, 0, :, :, z_axis]  # Tumor core
+    wt = data['pred'][0, 1, :, :, z_axis]  # Whole tumor
+    et = data['pred'][0, 2, :, :, z_axis]  # Enhancing tumor
+    
+    return [t1c, t1, t2, flair], [tc, wt, et] 
+
+
+# Use blocks to set up a more complex demo
+with gr.Blocks() as demo:
+
+    # Show title and description
+    gr.Markdown(title)
+    gr.Markdown(description)
+
+    with gr.Row():
+        # Get the input file and slice slider as inputs
+        input_file = gr.File(label='input file')
+        z_axis = gr.Slider(0, 200, label='slice', value=50)
+        
+    with gr.Row():
+        # Show the button with custom label
+        button = gr.Button("Segment Tumor!")
+
+    with gr.Row():
+        with gr.Column():
+            # Show the input image with different MR sequences
+            input_image = gr.Gallery(label='input MRI sequences (T1+, T1, T2, FLAIR)')
+        
+        with gr.Column():
+            # Show the segmentation labels
+            output_segmentation = gr.Gallery(label='output segmentations (TC, WT, ET)')
+
+    
+    # Run prediction on button click
+    button.click(
+        predict, 
+        inputs=[input_file, z_axis],
+        outputs=[input_image, output_segmentation]
+    )
+
+    # Have some example for the user to try out
+    examples = gr.Examples(
+        examples=examples,
+        inputs=[input_file, z_axis],
+        outputs=[input_image, output_segmentation],
+        fn=predict,
+        cache_examples=False
+    )
+    
+    # Show references at the bottom of the demo
+    gr.Markdown(references)
+
+
+# Launch the demo 
+demo.launch()

examples/BRATS_485.nii.gz

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examples/BRATS_486.nii.gz

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requirements.txt

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+git+https://github.com/katielink/MONAI.git@4042-download-hf-hub-bundle
+nibabel