BraTS-PED/view_brats.py

# # FIRST, CREATE A NEW PYTHON FILE AND RUN THIS EXACT CODE
# # Save this as view_brats.py and run it

# import nibabel as nib
# import numpy as np
# import matplotlib.pyplot as plt
# import os

# # ==== CHANGE THIS PATH TO YOUR ACTUAL PATH ====
# patient_path = r"D:\BraTS-PEDs-v1\Training\BraTS-PED-00031-000"

# # List all files in the folder
# print("Files in patient folder:")
# for file in os.listdir(patient_path):
#     print(f"  - {file}")

# # Load and visualize each modality
# modalities = {
#     't1n': 'T1 Native',
#     't1c': 'T1 Contrast (T1CE)',
#     't2w': 'T2 Weighted',
#     't2f': 'T2 FLAIR',
#     'seg': 'Segmentation Mask'
# }

# plt.figure(figsize=(15, 8))

# for i, (modality, title) in enumerate(modalities.items()):
#     # Construct file path
#     file_path = os.path.join(patient_path, f"BraTS-PED-00031-000-{modality}.nii.gz")
    
#     # Load the NIfTI file
#     img = nib.load(file_path)
#     data = img.get_fdata()
    
#     # Get middle slice (axial view)
#     middle_slice = data.shape[2] // 2
#     slice_data = data[:, :, middle_slice]
    
#     # Plot
#     plt.subplot(2, 3, i+1)
#     if modality == 'seg':
#         plt.imshow(slice_data.T, cmap='viridis', origin='lower')
#     else:
#         plt.imshow(slice_data.T, cmap='gray', origin='lower')
#     plt.title(f'{title}\nShape: {data.shape}')
#     plt.axis('off')
    
#     # Print information
#     print(f"\n{title}:")
#     print(f"  - Shape: {data.shape}")
#     print(f"  - Data type: {data.dtype}")
#     print(f"  - Min value: {data.min():.2f}")
#     print(f"  - Max value: {data.max():.2f}")
#     print(f"  - Mean value: {data.mean():.2f}")
    
#     # For segmentation mask, show unique values
#     if modality == 'seg':
#         unique_values = np.unique(data)
#         print(f"  - Unique labels: {unique_values}")
#         # BraTS labels: 0=background, 1=necrosis, 2=edema, 3=enhancing, 4=non-enhancing

# plt.tight_layout()
# plt.savefig('brats_visualization.png', dpi=150)
# plt.show()

# print("\n✅ Visualization complete! Check the saved image.")

import numpy as np
import nibabel as nib
import os
from collections import Counter

def analyze_tumor(seg_path):
    seg = nib.load(seg_path).get_fdata()
    unique, counts = np.unique(seg, return_counts=True)
    
    # BraTS 2024 PED labels:
    # 0: Background
    # 1: Necrosis
    # 2: Edema
    # 3: Enhancing tumor
    # 4: Non-enhancing tumor
    
    tumor_present = np.sum(seg > 0) > 0
    
    return {
        'total_voxels': seg.size,
        'tumor_voxels': np.sum(seg > 0),
        'tumor_ratio': np.sum(seg > 0) / seg.size * 100,
        'has_tumor': tumor_present,
        'class_distribution': dict(zip(unique, counts))
    }

# Analyze first patient
patient_folder = r"D:\BraTS-PEDs-v1\Training\BraTS-PED-00001-000"
seg_path = os.path.join(patient_folder, "BraTS-PED-00001-000-seg.nii.gz")

stats = analyze_tumor(seg_path)
print(f"Tumor present: {stats['has_tumor']}")
print(f"Tumor voxels: {stats['tumor_voxels']:,}")
print(f"Tumor ratio: {stats['tumor_ratio']:.4f}%")
print(f"Class distribution: {stats['class_distribution']}")