#!/usr/bin/env python3 """ Data Cleaning Script for Neonatal Brain MRI Dataset - Removes PII (patient name, hospital ID) - Translates column headers to English - Converts xlsx to CSV - Renames folders to English - Generates a combined dataset and cleaning report """ import os import shutil import csv import json from datetime import datetime import openpyxl # ============================================================ # Configuration # ============================================================ BASE_DIR = "/Users/yty/Desktop/med_paper/feng_dataset" # Mapping: original Chinese folder name -> English name FOLDER_MAP = { "正常 2026-2-6": "normal", "扩张 2026-2-6": "LVM", "软化 2026-2-6": "PVL", "扩张+软化 2026-2-7": "LVM_PVL", } # Mapping: original xlsx filename (inside each folder) -> group info XLSX_MAP = { "正常 2026-2-6": "正常 2026-2-6.xlsx", "扩张 2026-2-6": "扩张 2026-2-6.xlsx", "软化 2026-2-6": "软化 2026-2-6.xlsx", "扩张+软化 2026-2-7": "扩张+软化 2026-2-7 严老师.xlsx", } # Group labels GROUP_LABELS = { "normal": {"label": 0, "cn": "正常", "en": "Normal"}, "LVM": {"label": 1, "cn": "侧脑室扩张", "en": "Lateral Ventricular Megaly (LVM)"}, "PVL": {"label": 2, "cn": "脑白质软化", "en": "Periventricular Leukomalacia (PVL)"}, "LVM_PVL": {"label": 3, "cn": "扩张+软化", "en": "LVM + PVL"}, } # Target MRI channels TARGET_CHANNELS = ["FLAIR", "T1WI", "T2-SAG", "T2WI"] # Column name translation COLUMN_MAP = { "编号": "patient_id", "检查描述": "exam_description", "检查结果": "diagnosis", } # ============================================================ # Step 1: Process xlsx -> CSV (remove PII, translate headers) # ============================================================ def process_xlsx(folder_cn, folder_en): """Read xlsx, remove PII columns, save as CSV. Returns list of dicts.""" xlsx_path = os.path.join(BASE_DIR, folder_cn, XLSX_MAP[folder_cn]) wb = openpyxl.load_workbook(xlsx_path) ws = wb["Sheet1"] records = [] for row_idx in range(2, ws.max_row + 1): patient_id = ws.cell(row=row_idx, column=1).value exam_desc = ws.cell(row=row_idx, column=4).value diagnosis = ws.cell(row=row_idx, column=5).value if patient_id is None: continue # Clean text: strip whitespace, normalize patient_id = str(patient_id).strip() exam_desc = (exam_desc or "").strip().replace("\xa0", " ") diagnosis = (diagnosis or "").strip().replace("\xa0", " ") records.append({ "patient_id": patient_id, "group": folder_en, "group_label": GROUP_LABELS[folder_en]["label"], "group_name_en": GROUP_LABELS[folder_en]["en"], "exam_description": exam_desc, "diagnosis": diagnosis, }) return records # ============================================================ # Step 2: Rename folders # ============================================================ def rename_folders(): """Rename Chinese folder names to English.""" renamed = [] for cn_name, en_name in FOLDER_MAP.items(): src = os.path.join(BASE_DIR, cn_name) dst = os.path.join(BASE_DIR, en_name) if os.path.exists(src) and not os.path.exists(dst): os.rename(src, dst) renamed.append((cn_name, en_name)) elif os.path.exists(dst): renamed.append((cn_name, f"{en_name} (already exists)")) return renamed # ============================================================ # Step 3: Scan data integrity # ============================================================ def scan_integrity(folder_en): """Check each patient folder for target channels and count DCM files.""" folder_path = os.path.join(BASE_DIR, folder_en) patients = sorted([ d for d in os.listdir(folder_path) if os.path.isdir(os.path.join(folder_path, d)) and not d.startswith(".") ]) results = [] for p in patients: p_path = os.path.join(folder_path, p) subdirs = [ d for d in os.listdir(p_path) if os.path.isdir(os.path.join(p_path, d)) and not d.startswith(".") ] channel_info = {} missing_channels = [] extra_channels = [] for ch in TARGET_CHANNELS: ch_path = os.path.join(p_path, ch) if os.path.isdir(ch_path): dcm_count = len([ f for f in os.listdir(ch_path) if f.upper().endswith(".DCM") ]) channel_info[ch] = dcm_count else: missing_channels.append(ch) channel_info[ch] = 0 for d in subdirs: if d not in TARGET_CHANNELS: extra_channels.append(d) results.append({ "patient_id": p, "channels": channel_info, "missing_channels": missing_channels, "extra_channels": extra_channels, "total_target_dcm": sum(channel_info.values()), }) return results # ============================================================ # Main # ============================================================ def main(): print("=" * 60) print(" Neonatal Brain MRI Dataset - Data Cleaning") print("=" * 60) # --- Step 1: Process xlsx files --- print("\n[1/4] Processing xlsx files -> CSV ...") all_records = [] group_stats = {} for cn_name, en_name in FOLDER_MAP.items(): records = process_xlsx(cn_name, en_name) all_records.extend(records) group_stats[en_name] = len(records) print(f" {cn_name} -> {en_name}: {len(records)} records") # --- Step 2: Rename folders --- print("\n[2/4] Renaming folders to English ...") renamed = rename_folders() for cn, en in renamed: print(f" {cn} -> {en}") # --- Step 3: Save individual and combined CSVs --- print("\n[3/4] Saving CSV files ...") csv_fields = ["patient_id", "group", "group_label", "group_name_en", "exam_description", "diagnosis"] # Individual CSVs for en_name in FOLDER_MAP.values(): group_records = [r for r in all_records if r["group"] == en_name] csv_path = os.path.join(BASE_DIR, en_name, "clinical_data.csv") with open(csv_path, "w", newline="", encoding="utf-8") as f: writer = csv.DictWriter(f, fieldnames=csv_fields) writer.writeheader() writer.writerows(group_records) print(f" Saved: {en_name}/clinical_data.csv ({len(group_records)} records)") # Combined CSV combined_path = os.path.join(BASE_DIR, "clinical_data_all.csv") with open(combined_path, "w", newline="", encoding="utf-8") as f: writer = csv.DictWriter(f, fieldnames=csv_fields) writer.writeheader() writer.writerows(all_records) print(f" Saved: clinical_data_all.csv ({len(all_records)} total records)") # --- Step 4: Integrity scan & report --- print("\n[4/4] Scanning data integrity ...") report_lines = [] report_lines.append("# Data Cleaning Report") report_lines.append(f"**Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n") report_lines.append("## 1. Dataset Overview\n") report_lines.append("| Group | Folder | Patients | Label |") report_lines.append("|-------|--------|----------|-------|") for en_name, info in GROUP_LABELS.items(): count = group_stats.get(en_name, 0) report_lines.append( f"| {info['en']} | `{en_name}/` | {count} | {info['label']} |" ) report_lines.append(f"\n**Total patients**: {len(all_records)}\n") report_lines.append("## 2. Data Cleaning Actions\n") report_lines.append("| Action | Details |") report_lines.append("|--------|---------|") report_lines.append("| Removed PII | `姓名` (patient name), `住院号` (hospital ID) |") report_lines.append("| Column translation | `编号`→`patient_id`, `检查描述`→`exam_description`, `检查结果`→`diagnosis` |") report_lines.append("| Added columns | `group`, `group_label` (0-3), `group_name_en` |") report_lines.append("| Format conversion | `.xlsx` → `.csv` (UTF-8) |") report_lines.append("| Folder renaming | Chinese → English (see below) |") report_lines.append("| Text cleaning | Removed non-breaking spaces (`\\xa0`), stripped whitespace |\n") report_lines.append("### Folder Renaming\n") report_lines.append("| Original (Chinese) | New (English) |") report_lines.append("|-------------------|---------------|") for cn, en in FOLDER_MAP.items(): report_lines.append(f"| `{cn}` | `{en}` |") report_lines.append("\n## 3. MRI Channel Integrity\n") report_lines.append(f"**Target channels**: {', '.join(TARGET_CHANNELS)}\n") total_issues = 0 all_integrity = {} for en_name in FOLDER_MAP.values(): integrity = scan_integrity(en_name) all_integrity[en_name] = integrity issues = [r for r in integrity if r["missing_channels"]] total_issues += len(issues) total_dcm = sum(r["total_target_dcm"] for r in integrity) avg_dcm = total_dcm / len(integrity) if integrity else 0 report_lines.append(f"### {en_name} ({GROUP_LABELS[en_name]['en']})\n") report_lines.append(f"- Patients: {len(integrity)}") report_lines.append(f"- Total target-channel DCM files: {total_dcm}") report_lines.append(f"- Average DCM per patient (4 channels): {avg_dcm:.1f}") if issues: report_lines.append(f"- **Missing channels ({len(issues)} patients)**:") for r in issues: report_lines.append( f" - `{r['patient_id']}`: missing {', '.join(r['missing_channels'])}" ) else: report_lines.append("- All patients have complete target channels") # Extra channels summary all_extras = set() for r in integrity: all_extras.update(r["extra_channels"]) if all_extras: report_lines.append( f"- Extra channels present (not used): {', '.join(sorted(all_extras))}" ) report_lines.append("") report_lines.append("## 4. Output File Structure\n") report_lines.append("```") report_lines.append("feng_dataset/") report_lines.append("├── clinical_data_all.csv # Combined dataset (all groups)") report_lines.append("├── clean_data.py # This cleaning script") report_lines.append("├── cleaning_report.md # This report") report_lines.append("├── normal/ # Normal controls") report_lines.append("│ ├── clinical_data.csv") report_lines.append("│ └── 001-441/ # Patient folders") report_lines.append("│ ├── FLAIR/ (*.DCM)") report_lines.append("│ ├── T1WI/ (*.DCM)") report_lines.append("│ ├── T2-SAG/ (*.DCM)") report_lines.append("│ └── T2WI/ (*.DCM)") report_lines.append("├── LVM/ # Lateral Ventricular Megaly") report_lines.append("│ ├── clinical_data.csv") report_lines.append("│ └── 100-xxx/ ...") report_lines.append("├── PVL/ # Periventricular Leukomalacia") report_lines.append("│ ├── clinical_data.csv") report_lines.append("│ └── 010-xxx/ ...") report_lines.append("└── LVM_PVL/ # LVM + PVL") report_lines.append(" ├── clinical_data.csv") report_lines.append(" └── 110-xxx/ ...") report_lines.append("```\n") report_lines.append("## 5. CSV Column Description\n") report_lines.append("| Column | Type | Description |") report_lines.append("|--------|------|-------------|") report_lines.append("| `patient_id` | string | Unique patient identifier (e.g., `001-441`) |") report_lines.append("| `group` | string | Group folder name: `normal`, `LVM`, `PVL`, `LVM_PVL` |") report_lines.append("| `group_label` | int | Numeric label: 0=Normal, 1=LVM, 2=PVL, 3=LVM+PVL |") report_lines.append("| `group_name_en` | string | Full English group name |") report_lines.append("| `exam_description` | string | Radiologist's MRI examination description (Chinese) |") report_lines.append("| `diagnosis` | string | Final diagnosis conclusion (Chinese) |\n") report_lines.append("## 6. Notes\n") report_lines.append("- **Medical text** (`exam_description`, `diagnosis`) is kept in the original Chinese " "to preserve clinical accuracy. Machine translation of specialized medical " "radiology reports may introduce errors.") report_lines.append("- **Original xlsx files** are retained in each folder as backup.") report_lines.append(f"- **Data integrity issues**: {total_issues} patient(s) with missing target channels.") report_lines.append("- **Disease description document**: `新生儿侧脑室扩张+脑白质软化疾病描述 AI+医生审核版本.docx` " "is preserved as-is (reference document, not patient data).") # Write report report_path = os.path.join(BASE_DIR, "cleaning_report.md") with open(report_path, "w", encoding="utf-8") as f: f.write("\n".join(report_lines)) print(f" Report saved: cleaning_report.md") # Summary print("\n" + "=" * 60) print(" CLEANING COMPLETE") print("=" * 60) print(f" Total patients: {len(all_records)}") print(f" Groups: {len(FOLDER_MAP)}") print(f" Integrity issues: {total_issues} patient(s) with missing channels") print(f" Output files:") print(f" - clinical_data_all.csv (combined)") print(f" - */clinical_data.csv (per-group)") print(f" - cleaning_report.md (report)") print() if __name__ == "__main__": main()