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"""
Example usage of CarSONSegmentor for cardiac MRI segmentation.
This script demonstrates how to use the ONNX-based CarSON segmentor
to segment cardiac structures from MRI images.
"""
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
import sys
# Add parent directory to path for imports
sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent))
from cardiac_toolkit.segmentation import CarSONSegmentor
def example_basic_usage():
"""Example 1: Basic 2D segmentation."""
print("=" * 80)
print("Example 1: Basic 2D Segmentation")
print("=" * 80)
# Initialize segmentor
# Default model path (update with your actual path)
model_path = "/home/jx332/project/code/2025-05-DeepStrain/pretrained_models/carson_Jan2021.onnx"
if not Path(model_path).exists():
print(f"\n⚠ Model not found at {model_path}")
print("Please update the model path in this script.")
return None, None
segmentor = CarSONSegmentor(model_path=model_path)
# Create dummy image (replace with your own image loading)
# In practice: image = np.load('your_cardiac_mri.npy')
image = np.random.rand(256, 256).astype(np.float32) * 1000
print(f"\nInput image shape: {image.shape}")
print(f"Input image dtype: {image.dtype}")
print(f"Input image range: [{image.min():.2f}, {image.max():.2f}]")
# Perform segmentation
print("\nPerforming segmentation...")
mask = segmentor.segment(image)
print(f"\nOutput mask shape: {mask.shape}")
print(f"Output mask dtype: {mask.dtype}")
print(f"Unique labels: {np.unique(mask)}")
# Count pixels per class
print("\nPixel counts per class:")
for idx, name in segmentor.get_class_names().items():
count = (mask == idx).sum()
print(f" {name} (class {idx}): {count} pixels")
return image, mask
def example_with_probabilities():
"""Example 2: Getting probability maps."""
print("\n" + "=" * 80)
print("Example 2: Probability Maps")
print("=" * 80)
# Initialize segmentor
model_path = "/home/jx332/project/code/2025-05-DeepStrain/pretrained_models/carson_Jan2021.onnx"
if not Path(model_path).exists():
print(f"\n⚠ Model not found at {model_path}")
return None, None
segmentor = CarSONSegmentor(model_path=model_path)
# Create dummy image
image = np.random.rand(256, 256).astype(np.float32) * 1000
print(f"\nInput image shape: {image.shape}")
# Get probability maps
print("\nGetting probability maps...")
probs = segmentor.predict(image, return_probs=True)
print(f"\nOutput probabilities shape: {probs.shape}")
print(f"Probability range: [{probs.min():.3f}, {probs.max():.3f}]")
# Check that probabilities sum to 1 (in center region where predictions exist)
h, w = image.shape
center_h, center_w = h // 2, w // 2
center_probs = probs[center_h-50:center_h+50, center_w-50:center_w+50, :]
prob_sum = center_probs.sum(axis=-1)
print(f"\nProbability sum per pixel (center region):")
print(f" Mean: {prob_sum.mean():.6f}")
print(f" Std: {prob_sum.std():.6f}")
return image, probs
def example_3d_volume():
"""Example 3: 3D volume segmentation."""
print("\n" + "=" * 80)
print("Example 3: 3D Volume Segmentation")
print("=" * 80)
# Initialize segmentor
model_path = "/home/jx332/project/code/2025-05-DeepStrain/pretrained_models/carson_Jan2021.onnx"
if not Path(model_path).exists():
print(f"\n⚠ Model not found at {model_path}")
return None, None
segmentor = CarSONSegmentor(model_path=model_path, batch_size=16)
# Create dummy 3D volume
volume = np.random.rand(256, 256, 10).astype(np.float32) * 1000
print(f"\nInput volume shape: {volume.shape}")
# Segment entire volume
print("\nSegmenting 3D volume...")
mask_3d = segmentor.segment(volume)
print(f"\nOutput mask shape: {mask_3d.shape}")
print(f"Unique labels: {np.unique(mask_3d)}")
# Count pixels per class across all slices
print("\nPixel counts per class (all slices):")
for idx, name in segmentor.get_class_names().items():
count = (mask_3d == idx).sum()
print(f" {name} (class {idx}): {count} voxels")
return volume, mask_3d
def example_visualization():
"""Example 4: Visualizing results."""
print("\n" + "=" * 80)
print("Example 4: Visualization")
print("=" * 80)
# Initialize segmentor
model_path = "/home/jx332/project/code/2025-05-DeepStrain/pretrained_models/carson_Jan2021.onnx"
if not Path(model_path).exists():
print(f"\n⚠ Model not found at {model_path}")
return
segmentor = CarSONSegmentor(model_path=model_path)
# Create dummy image
image = np.random.rand(256, 256).astype(np.float32) * 1000
# Perform segmentation
mask = segmentor.segment(image)
probs = segmentor.predict(image, return_probs=True)
# Create visualization
print("\nCreating visualization...")
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
# Original image
axes[0, 0].imshow(image, cmap='gray')
axes[0, 0].set_title('Original Image')
axes[0, 0].axis('off')
# Segmentation mask
axes[0, 1].imshow(mask, cmap='jet', vmin=0, vmax=3)
axes[0, 1].set_title('Segmentation\n(0=BG, 1=LV, 2=Myo, 3=RV)')
axes[0, 1].axis('off')
# Overlay
axes[0, 2].imshow(image, cmap='gray')
axes[0, 2].imshow(mask, cmap='jet', alpha=0.4, vmin=0, vmax=3)
axes[0, 2].set_title('Overlay')
axes[0, 2].axis('off')
# Class probabilities
class_names = ['Background', 'LV Cavity', 'Myocardium']
for i in range(3):
ax = axes[1, i]
im = ax.imshow(probs[:, :, i+1], cmap='hot', vmin=0, vmax=1)
ax.set_title(f'{class_names[i]} Probability')
ax.axis('off')
plt.colorbar(im, ax=ax, fraction=0.046)
plt.tight_layout()
# Save figure
output_path = Path(__file__).parent / 'carson_segmentation_result.png'
plt.savefig(output_path, dpi=150, bbox_inches='tight')
print(f"\nVisualization saved to: {output_path}")
plt.close()
def main():
"""Run all examples."""
print("\n" + "*" * 80)
print("CarSONSegmentor Examples")
print("*" * 80)
try:
# Example 1: Basic usage
example_basic_usage()
# Example 2: Probability maps
example_with_probabilities()
# Example 3: 3D volume
example_3d_volume()
# Example 4: Visualization
example_visualization()
print("\n" + "=" * 80)
print("All examples completed successfully!")
print("=" * 80)
print()
except Exception as e:
print(f"\n\nError running examples: {e}")
import traceback
traceback.print_exc()
if __name__ == '__main__':
main()
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