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9ee6a3d | 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 | #!/usr/bin/env python3
"""
Calculate muscle fat percentages from CT images using model predictions
"""
import os
import re
from pathlib import Path
import numpy as np # type: ignore
import pandas as pd # type: ignore
import nibabel as nib # type: ignore
root_100_120 = Path("../100-120")
output_csv_100_120 = Path("../fatty_data/model_pred_dev.csv")
fat_hu_thresh = -20
muscle_labels = {
1: "psoas",
2: "quadratus_lumborum",
3: "paraspinal",
4: "latissimus_dorsi",
5: "iliacus",
6: "rectus_femoris",
7: "vastus",
8: "rhomboid",
9: "trapezius",
}
def load_image_and_label_100_120(case_id: int, root_dir: Path):
"""Load CT image and model prediction label file for cases 100-120."""
img_path = root_dir / "images_100-120" / f"{case_id}_0000.nii.gz"
if not img_path.exists():
return None, None
lab_path = root_dir / "label_9_muscles_model_pred" / f"{case_id}.nii.gz"
if not lab_path.exists():
return None, None
try:
img = nib.load(str(img_path)) # type: ignore
lab = nib.load(str(lab_path)) # type: ignore
return img.get_fdata(), lab.get_fdata()
except Exception as e:
print(f"Error loading case {case_id}: {e}")
return None, None
def calculate_fat_percentages(img_arr: np.ndarray, lab_arr: np.ndarray):
"""Calculate fat percentage (HU <= -20) for all 9 muscle labels."""
fat_mask = img_arr <= fat_hu_thresh
fat_percentages = {}
for label_id, muscle_name in muscle_labels.items():
muscle_mask = (lab_arr == label_id)
total_voxels = int(np.count_nonzero(muscle_mask)) # type: ignore
if total_voxels == 0:
fat_pct = 0.0
else:
fat_voxels = int(np.count_nonzero(fat_mask & muscle_mask)) # type: ignore
fat_pct = (fat_voxels / total_voxels) * 100.0
fat_percentages[f"{muscle_name}_fat_pct"] = round(fat_pct, 2)
return fat_percentages
def save_mean_std_stats(df, fat_cols, output_path):
"""Save mean ± SD statistics to a separate CSV file."""
stats_data = []
for col in fat_cols:
values = df[col].values
mean_val = np.mean(values) # type: ignore
std_val = np.std(values) # type: ignore
stats_data.append({
'muscle': col.replace('_fat_pct', ''),
'mean': round(mean_val, 2),
'std': round(std_val, 2),
'mean_std': f"{mean_val:.2f} ± {std_val:.2f}"
})
stats_df = pd.DataFrame(stats_data)
stats_df.to_csv(output_path, index=False)
print(f"Mean ± SD statistics saved to: {output_path}")
def process_100_120_dataset():
"""Process cases 100-120 using model predictions and save to model_pred_dev.csv"""
print("="*60)
print("PROCESSING CASES 100-120 WITH MODEL PREDICTIONS")
print("="*60)
rows = []
print("Processing cases 100-120 with model predictions...")
for case_id in range(100, 121):
img_arr, lab_arr = load_image_and_label_100_120(case_id, root_100_120)
if img_arr is not None and lab_arr is not None:
fat_percentages = calculate_fat_percentages(img_arr, lab_arr)
record = {"case_id": case_id, "dataset": "100-120_model_pred"}
record.update(fat_percentages)
rows.append(record)
print(f"Case {case_id}: {[v for v in fat_percentages.values()][:3]}... %")
else:
print(f"Case {case_id}: Failed to load")
if rows:
df = pd.DataFrame(rows)
fat_cols = [col for col in df.columns if col.endswith("_fat_pct")]
fat_means = df[fat_cols].mean().round(2)
fat_stds = df[fat_cols].std().round(2)
summary_row = {"case_id": "Mean ± SD"}
for col in fat_cols:
summary_row[col] = f"{fat_means[col]:.2f} ± {fat_stds[col]:.2f}"
summary_df = pd.DataFrame([summary_row])
df_with_summary = pd.concat([df, summary_df], ignore_index=True) # type: ignore
output_csv_100_120.parent.mkdir(parents=True, exist_ok=True)
df_with_summary.to_csv(output_csv_100_120, index=False)
print(f"\nSaved {len(rows)} cases to {output_csv_100_120}")
print(f"\nFat Percentage Summary (100-120 Model Predictions):")
for col in fat_cols:
values = df[col].values
mean_val = np.mean(values) # type: ignore
std_val = np.std(values) # type: ignore
print(f"{col}: {mean_val:.2f} ± {std_val:.2f} %")
save_mean_std_stats(df, fat_cols, output_csv_100_120.parent / "model_pred_dev_mean_std.csv")
else:
print("No data processed for 100-120!")
def main():
"""Main function to process the development dataset with model predictions."""
print("FATTY PERCENTAGE ANALYSIS - MODEL PREDICTIONS")
print("Computing fat percentage (HU <= -20) for 9 muscle labels using model predictions")
print("="*60)
process_100_120_dataset()
print("\n" + "="*60)
print("ANALYSIS COMPLETE")
print("="*60)
print(f"Results saved to:")
print(f" - {output_csv_100_120} (cases 100-120 with model predictions)")
print(f" - {output_csv_100_120.parent / 'model_pred_dev_mean_std.csv'} (summary statistics)")
if __name__ == "__main__":
main()
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