Committed_For_Exemple1

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.nii filter=lfs diff=lfs merge=lfs -text
+last_state_dict[[:space:]].pth filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,127 @@
+import io
+import numpy as np
+import nibabel as nib
+import torch
+import gradio as gr
+import matplotlib.pyplot as plt
+from PIL import Image
+from monai.transforms import (
+    Compose, NormalizeIntensityd,
+    ResizeWithPadOrCropd, ToTensord
+)
+from monai.networks.nets import SwinUNETR
+
+
+model = SwinUNETR(img_size=(128, 128, 128), 
+                  in_channels=4, 
+                  out_channels=3, 
+                  feature_size=48, 
+                  use_checkpoint=True)
+
+model.load_dtate_dict(torch.load("last_state_dict .pth",map_location=torch.device('cpu'))) 
+
+model.eval()
+val_transform = Compose([
+    NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True),
+    ResizeWithPadOrCropd(keys="image", spatial_size=(128, 128, 128)),
+    ToTensord(keys="image")
+])
+
+# This will be set in predict_and_store
+cached_images = []
+cached_masks = []
+
+# Helper to generate an overlay image for one slice
+def get_overlay_figure(image, mask, slice_index):
+    img_slice = image[:, :, slice_index]
+    img_slice = (img_slice - img_slice.min()) / (img_slice.max() - img_slice.min())
+    img_rgb = np.stack([img_slice]*3, axis=-1)
+
+    mask_rgb = np.zeros_like(img_rgb)
+    mask_rgb[mask[:, :, slice_index] == 2] = [0, 0, 1]  # Blue: WT
+    mask_rgb[mask[:, :, slice_index] == 1] = [0, 1, 0]  # Green: TC
+    mask_rgb[mask[:, :, slice_index] == 4] = [1, 0, 0]  # Red: ET
+
+    overlay = np.clip((1 - 0.4) * img_rgb + 0.4 * mask_rgb, 0, 1)
+
+    fig, ax = plt.subplots(figsize=(6, 6))
+    ax.imshow(overlay)
+    ax.axis("off")
+    ax.set_title(f"Segmentation Overlay - Slice {slice_index}")
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    plt.close(fig)
+    return Image.open(buf)
+
+# Prediction function that stores the processed image and mask for slider use
+def predict_and_store(flair_file, t1_file, t1ce_file, t2_file):
+    global cached_images, cached_masks
+
+    flair = nib.load(flair_file.name).get_fdata()
+    t1 = nib.load(t1_file.name).get_fdata()
+    t1ce = nib.load(t1ce_file.name).get_fdata()
+    t2 = nib.load(t2_file.name).get_fdata()
+
+    image = np.stack([flair, t1, t1ce, t2], axis=0)
+    data = {"image": image}
+    data = val_transform(data)
+    image_tensor = data["image"].unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        output = model(image_tensor)
+        prob = torch.sigmoid(output)
+        seg = prob[0].detach().cpu().numpy()
+        seg = (seg > 0.5).astype(np.int8)
+        seg_out = np.zeros((seg.shape[1], seg.shape[2], seg.shape[3]))
+        seg_out[seg[1] == 1] = 2
+        seg_out[seg[0] == 1] = 1
+        seg_out[seg[2] == 1] = 4
+
+    cached_images = image_tensor.cpu().numpy()[0, 0]
+    cached_masks = seg_out
+    return f"Segmentation done. Use the slider to browse slices."
+
+# Function to get the overlay for a specific slice
+def get_slice_overlay(slice_index):
+    global cached_images, cached_masks
+    if len(cached_images) == 0:
+        return None
+    return get_overlay_figure(cached_images, cached_masks, slice_index)
+
+with gr.Blocks() as iface:
+    gr.Markdown("### 🧠 SwinUNETR Brain Tumor Segmentation Viewer")
+
+    gr.Markdown("#### 📂 Upload MRI Modalities")
+    with gr.Row():
+        flair = gr.File(file_types=[".nii", ".nii.gz"], label="FLAIR (.nii)")
+        t1 = gr.File(file_types=[".nii", ".nii.gz"], label="T1 (.nii)")
+        t1ce = gr.File(file_types=[".nii", ".nii.gz"], label="T1ce (.nii)")
+        t2 = gr.File(file_types=[".nii", ".nii.gz"], label="T2 (.nii)")
+
+    # Ajout de la section exemples
+    gr.Markdown("#### 🧪 Or use example data")
+    examples = gr.Examples(
+        examples=[
+            ["examples/flair.nii", "examples/t1.nii", "examples/t1ce.nii", "examples/t2.nii"]
+        ],
+        inputs=[flair, t1, t1ce, t2],
+        label="Example MRI files"
+    )
+
+    output_msg = gr.Textbox(label="📣 Status")
+    run_button = gr.Button("▶️ Run Segmentation")
+
+    run_button.click(fn=predict_and_store,
+                     inputs=[flair, t1, t1ce, t2],
+                     outputs=output_msg)
+
+    gr.Markdown("### 🖼️ View Slices with Overlay")
+    slice_slider = gr.Slider(minimum=0, maximum=127, value=64, step=1, label="Slice Index")
+    slice_image = gr.Image(type="pil", label="Segmentation Overlay")
+
+    slice_slider.change(fn=get_slice_overlay,
+                        inputs=slice_slider,
+                        outputs=slice_image)
+
+iface.launch()

examples/flair.nii

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69f04d7db20f5fd64f8616bcda2079573253208850102b86c7bc86dbdab1e08d
+size 17858880

examples/t1.nii

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0f30a8d82039ae19ca79611ff6809b7b6e839ef2a5580f48c6c976bbbb3ed976
+size 17858880

examples/t1ce.nii

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d172fcebe1a37782706f2e11df73c6cbb46f79b8261a2b3188d7ad377cd24533
+size 17858880

examples/t2.nii

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13747faa70e519e3a27fd643f526377132a65c8da12465048535301feaa33db6
+size 17858880

last_state_dict .pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f1e23bd9e8f68a7d358f4aa0269692b838227e073e698132f7642d99572c6d0
+size 256366028

requirements.txt

@@ -0,0 +1,12 @@
+# Interface
+gradio
+matplotlib
+Pillow
+
+# Traitement d'image médicale
+nibabel
+monai
+
+# Deep Learning
+torch
+numpy