File size: 9,059 Bytes
d5d23f9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 | #!/usr/bin/env python
"""
Example usage of JDLSegmentor for myocardium and scar segmentation.
This script demonstrates how to use the ONNX-based JDL segmentor
to segment myocardium and scar tissue from cardiac 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 JDLSegmentor
def example_basic_usage():
"""Example 1: Basic 2D segmentation."""
print("=" * 80)
print("Example 1: Basic 2D Segmentation")
print("=" * 80)
# Initialize segmentor
# Default model paths (update with your actual paths)
myocardium_model_path = "/gpfs/gibbs/project/kwan/jx332/code/2025-02-LGE-multiview-seg/checkpoints/jdl_myocardium.onnx"
scar_model_path = "/gpfs/gibbs/project/kwan/jx332/code/2025-02-LGE-multiview-seg/checkpoints/jdl_scar.onnx"
if not Path(myocardium_model_path).exists() or not Path(scar_model_path).exists():
print(f"\n⚠ Models not found")
print(f"Myocardium: {myocardium_model_path}")
print(f"Scar: {scar_model_path}")
print("Please update the model paths in this script.")
return None, None, None
segmentor = JDLSegmentor(
myocardium_model_path=myocardium_model_path,
scar_model_path=scar_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...")
myo_mask, scar_mask = segmentor.segment(image)
print(f"\nMyocardium mask shape: {myo_mask.shape}")
print(f"Myocardium pixels: {np.sum(myo_mask)}")
print(f"Scar mask shape: {scar_mask.shape}")
print(f"Scar pixels: {np.sum(scar_mask)}")
return image, myo_mask, scar_mask
def example_with_bbox():
"""Example 2: Segmentation with bounding box."""
print("\n" + "=" * 80)
print("Example 2: Segmentation with Bounding Box")
print("=" * 80)
# Initialize segmentor
myocardium_model_path = "/gpfs/gibbs/project/kwan/jx332/code/2025-02-LGE-multiview-seg/checkpoints/jdl_myocardium.onnx"
scar_model_path = "/gpfs/gibbs/project/kwan/jx332/code/2025-02-LGE-multiview-seg/checkpoints/jdl_scar.onnx"
if not Path(myocardium_model_path).exists() or not Path(scar_model_path).exists():
print(f"\n⚠ Models not found")
return None, None, None, None
segmentor = JDLSegmentor(
myocardium_model_path=myocardium_model_path,
scar_model_path=scar_model_path
)
# Create dummy image
image = np.random.rand(512, 512).astype(np.float32) * 1000
# Define a bounding box around the heart region
bbox = {
'x1': 150,
'y1': 150,
'x2': 350,
'y2': 350
}
print(f"\nInput image shape: {image.shape}")
print(f"Bounding box: {bbox}")
# Perform segmentation with bbox
print("\nPerforming segmentation with bounding box...")
myo_mask, scar_mask = segmentor.segment(
image,
bbox=bbox,
bbox_scale_factor=1.1 # Expand bbox by 10%
)
print(f"\nMyocardium mask shape: {myo_mask.shape}")
print(f"Myocardium pixels: {np.sum(myo_mask)}")
print(f"Scar mask shape: {scar_mask.shape}")
print(f"Scar pixels: {np.sum(scar_mask)}")
return image, myo_mask, scar_mask, bbox
def example_batch_processing():
"""Example 3: Batch processing of multiple slices."""
print("\n" + "=" * 80)
print("Example 3: Batch Processing")
print("=" * 80)
# Initialize segmentor
myocardium_model_path = "/gpfs/gibbs/project/kwan/jx332/code/2025-02-LGE-multiview-seg/checkpoints/jdl_myocardium.onnx"
scar_model_path = "/gpfs/gibbs/project/kwan/jx332/code/2025-02-LGE-multiview-seg/checkpoints/jdl_scar.onnx"
if not Path(myocardium_model_path).exists() or not Path(scar_model_path).exists():
print(f"\n⚠ Models not found")
return None, None, None
segmentor = JDLSegmentor(
myocardium_model_path=myocardium_model_path,
scar_model_path=scar_model_path
)
# Create dummy batch of images (e.g., multiple slices)
num_slices = 5
images = [np.random.rand(256, 256).astype(np.float32) * 1000 for _ in range(num_slices)]
print(f"\nNumber of slices: {num_slices}")
print(f"Each slice shape: {images[0].shape}")
# Process batch
print("\nProcessing batch...")
myo_masks, scar_masks = segmentor.segment_batch(images)
print(f"\nProcessed {len(myo_masks)} slices")
for i, (myo_mask, scar_mask) in enumerate(zip(myo_masks, scar_masks)):
print(f" Slice {i}: Myo pixels = {np.sum(myo_mask)}, Scar pixels = {np.sum(scar_mask)}")
return images, myo_masks, scar_masks
def example_3d_volume():
"""Example 4: 3D volume segmentation."""
print("\n" + "=" * 80)
print("Example 4: 3D Volume Segmentation")
print("=" * 80)
# Initialize segmentor
myocardium_model_path = "/gpfs/gibbs/project/kwan/jx332/code/2025-02-LGE-multiview-seg/checkpoints/jdl_myocardium.onnx"
scar_model_path = "/gpfs/gibbs/project/kwan/jx332/code/2025-02-LGE-multiview-seg/checkpoints/jdl_scar.onnx"
if not Path(myocardium_model_path).exists() or not Path(scar_model_path).exists():
print(f"\n⚠ Models not found")
return None, None, None
segmentor = JDLSegmentor(
myocardium_model_path=myocardium_model_path,
scar_model_path=scar_model_path
)
# Create dummy 3D volume
volume = np.random.rand(256, 256, 8).astype(np.float32) * 1000
print(f"\nInput volume shape: {volume.shape}")
# Segment 3D volume
print("\nSegmenting 3D volume...")
myo_mask_3d, scar_mask_3d = segmentor.segment_3d(
volume,
keep_largest_component='per_slice' # or 'whole_volume'
)
print(f"\nMyocardium mask shape: {myo_mask_3d.shape}")
print(f"Myocardium voxels: {np.sum(myo_mask_3d)}")
print(f"Scar mask shape: {scar_mask_3d.shape}")
print(f"Scar voxels: {np.sum(scar_mask_3d)}")
return volume, myo_mask_3d, scar_mask_3d
def example_visualization():
"""Example 5: Visualizing results."""
print("\n" + "=" * 80)
print("Example 5: Visualization")
print("=" * 80)
# Initialize segmentor
myocardium_model_path = "/gpfs/gibbs/project/kwan/jx332/code/2025-02-LGE-multiview-seg/checkpoints/jdl_myocardium.onnx"
scar_model_path = "/gpfs/gibbs/project/kwan/jx332/code/2025-02-LGE-multiview-seg/checkpoints/jdl_scar.onnx"
if not Path(myocardium_model_path).exists() or not Path(scar_model_path).exists():
print(f"\n⚠ Models not found")
return
segmentor = JDLSegmentor(
myocardium_model_path=myocardium_model_path,
scar_model_path=scar_model_path
)
# Create dummy image
image = np.random.rand(256, 256).astype(np.float32) * 1000
# Perform segmentation
print("\nPerforming segmentation...")
myo_mask, scar_mask = segmentor.segment(image)
# Create visualization
print("\nCreating visualization...")
fig, axes = plt.subplots(1, 4, figsize=(16, 4))
# Original image
axes[0].imshow(image, cmap='gray')
axes[0].set_title('Original Image')
axes[0].axis('off')
# Myocardium mask
axes[1].imshow(myo_mask, cmap='gray')
axes[1].set_title('Myocardium Mask')
axes[1].axis('off')
# Scar mask
axes[2].imshow(scar_mask, cmap='gray')
axes[2].set_title('Scar Mask')
axes[2].axis('off')
# Overlay
axes[3].imshow(image, cmap='gray')
axes[3].imshow(myo_mask, cmap='Reds', alpha=0.3)
axes[3].imshow(scar_mask, cmap='Blues', alpha=0.3)
axes[3].set_title('Overlay\n(Red=Myo, Blue=Scar)')
axes[3].axis('off')
plt.tight_layout()
# Save figure
output_path = Path(__file__).parent / 'jdl_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("JDLSegmentor Examples")
print("*" * 80)
try:
# Example 1: Basic usage
example_basic_usage()
# Example 2: With bounding box
example_with_bbox()
# Example 3: Batch processing
example_batch_processing()
# Example 4: 3D volume
example_3d_volume()
# Example 5: 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()
|