#!/usr/bin/env python3 """ Calculate muscle fat percentages from CT images """ 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") label_root_251_500 = Path("../model_training/251-500_out") image_root_251_500 = Path("../model_training/251-500_in") output_csv_100_120 = Path("../fatty_data/dev_fat.csv") output_csv_251_500 = Path("../fatty_data/test_fat.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 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 / "labels_9_muscles" / 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 load_image_and_label_251_500(case_id: int): """Load CT image and label file for cases 251-500.""" img_path = image_root_251_500 / f"AtlasDataset_{case_id:06d}_0000.nii.gz" if not img_path.exists(): print(f"Image not found: {img_path}") return None, None lab_path = label_root_251_500 / f"AtlasDataset_{case_id:06d}.nii.gz" if not lab_path.exists(): print(f"Label not found: {lab_path}") 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 extract_case_ids_from_labels(): """Extract case IDs from label folder files (251-500).""" case_ids = [] if not label_root_251_500.exists(): print(f"Label folder not found: {label_root_251_500}") return case_ids pattern = re.compile(r'AtlasDataset_(\d+)\.nii\.gz') for file_path in label_root_251_500.glob("*.nii.gz"): match = pattern.match(file_path.name) if match: case_id = int(match.group(1)) case_ids.append(case_id) return sorted(case_ids) 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 and save to fatty_atrophy.csv""" print("="*60) print("PROCESSING CASES 100-120") print("="*60) rows = [] print("Processing cases 100-120...") 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"} 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):") 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 / "dev_fat_mean_std.csv") else: print("No data processed for 100-120!") def process_251_500_dataset(): """Process cases 251-500 and save to test_fat.csv""" print("\n" + "="*60) print("PROCESSING CASES 251-500") print("="*60) print("Extracting case IDs from label folder...") case_ids = extract_case_ids_from_labels() if not case_ids: print("No case IDs found in label folder!") return print(f"Found {len(case_ids)} cases in label folder: {case_ids[:5]}...{case_ids[-5:]}") rows = [] processed_count = 0 failed_count = 0 print(f"\nProcessing {len(case_ids)} cases...") for i, case_id in enumerate(case_ids, 1): print(f"Processing case {case_id} ({i}/{len(case_ids)})...") img_arr, lab_arr = load_image_and_label_251_500(case_id) 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": "251-500"} record.update(fat_percentages) rows.append(record) processed_count += 1 sample_values = [v for v in fat_percentages.values()][:3] print(f" Case {case_id}: {sample_values}... %") else: failed_count += 1 print(f" Case {case_id}: Failed to load") print(f"\nProcessing complete: {processed_count} successful, {failed_count} failed") 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_251_500.parent.mkdir(parents=True, exist_ok=True) df_with_summary.to_csv(output_csv_251_500, index=False) print(f"\nSaved {len(rows)} cases to {output_csv_251_500}") print(f"\nFat Percentage Summary (251-500):") 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_251_500.parent / "test_fat_mean_std.csv") else: print("No data processed for 251-500!") def main(): """Main function to process both datasets.""" print("FATTY PERCENTAGE ANALYSIS") print("Computing fat percentage (HU <= -20) for 9 muscle labels") print("="*60) process_100_120_dataset() process_251_500_dataset() print("\n" + "="*60) print("ANALYSIS COMPLETE") print("="*60) print(f"Results saved to:") print(f" - {output_csv_100_120} (cases 100-120)") print(f" - {output_csv_251_500} (cases 251-500)") if __name__ == "__main__": main()