23-feature-request-glioma-segmentation-tutorial-with-brats-toolkit

BraTS-Toolkit/README.md

@@ -0,0 +1,3 @@
+
+# Preprocessing tutorials
+This folder contains tutorials for the [BraTS Toolkit package](https://github.com/neuronflow/BraTS-Toolkit). Please have a look at the Jupyter notebooks.

BraTS-Toolkit/temporary_directory/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

BraTS-Toolkit/utils.py

@@ -0,0 +1,43 @@
+import os
+import matplotlib.pyplot as plt
+import nibabel as nib
+
+DATA_FOLDER = "data"
+
+
+def visualize_data(data_folder: str = DATA_FOLDER, slice_index: int = 75):
+    """Visualize the MRI modalities for a given slice index
+
+    Args:
+        data_folder (str, optional): Path to the folder containing the t1, t1c, t2 & flair file. Defaults to DATA_FOLDER.
+        slice_index (int, optional): Slice to be visualized (first index in data of shape (155, 240, 240)). Defaults to 75.
+    """
+    _, axes = plt.subplots(1, 4, figsize=(12, 10))
+
+    modalities = ["t1", "t1c", "t2", "flair"]
+    for i, mod in enumerate(modalities):
+        modality_file = os.path.join(data_folder, f"{mod}.nii.gz")
+        modality_np = nib.load(modality_file).get_fdata().transpose(2, 1, 0)
+        axes[i].set_title(mod)
+        axes[i].imshow(modality_np[slice_index, :, :], cmap="gray")
+        axes[i].axis("off")
+
+
+def visualize_segmentation(modality_file: str, segmentation_file: str):
+    """Visualize the MRI modality and the segmentation
+
+    Args:
+        modality_file (str): Path to the desired modality file
+        segmentation_file (str): Path to the segmentation file
+    """
+    modality_np = nib.load(modality_file).get_fdata().transpose(2, 1, 0)
+    seg_np = nib.load(segmentation_file).get_fdata().transpose(2, 1, 0)
+    _, ax = plt.subplots(1, 2, figsize=(8, 4))
+
+    slice_index = modality_np.shape[0] // 2  # You can choose any slice here
+    ax[0].imshow(modality_np[slice_index, :, :], cmap="gray")
+    ax[1].imshow(modality_np[slice_index, :, :], cmap="gray")
+    ax[1].imshow(seg_np[slice_index, :, :], cmap="plasma", alpha=0.3)
+    for ax in ax:
+        ax.axis("off")
+    plt.tight_layout()