TODO.md

📋 TODO List: Pneumonia Consolidation Segmentation Project

✅ Completed

• Analyze project structure and patient data format
• Create preprocessing script for consolidation enhancement
• Build Streamlit app for Dice score calculation
• Implement SAM integration for automatic segmentation
• Create requirements.txt and documentation
• Setup folder structure for annotations and results

🚀 Next Steps (In Order)

Phase 1: Setup & Data Preparation (Week 1)

1. Install Dependencies

   • Run pip install -r requirements.txt
   • Test Streamlit app: streamlit run dice_calculator_app.py
   • (Optional) Download SAM checkpoint for automatic segmentation

2. Preprocess Patient Images

   • Enhance all chest X-rays in data/Pacientes/ folder
   • Save enhanced images to dice/enhanced_images/
   • Review enhanced images for quality
   • Document any images with poor quality

3. Setup Annotation Tool

   • Install CVAT (recommended) or Label Studio
   • Import enhanced images into annotation tool
   • Create annotation classes: "consolidation", "ground_glass", "air_bronchogram"
   • Setup annotation guidelines document for team

Phase 2: Annotation (Weeks 2-4)

4. Create Ground Truth Annotations

   • Have 2-3 radiologists independently annotate same 20 images (pilot)
   • Calculate inter-rater agreement using Dice scores
   • Resolve disagreements through consensus meeting
   • Annotate remaining images (aim for 100+ cases)
   • Save masks to dice/annotations/ground_truth/

5. Quality Control

   • Use Dice calculator app to validate annotation consistency
   • Flag cases with unclear consolidation boundaries
   • Re-annotate cases with Dice < 0.70 between annotators
   • Document difficult cases and edge cases

Phase 3: SAM Integration (Week 5)

6. Test SAM for Automatic Segmentation

   • Download SAM checkpoint (ViT-H recommended)
   • Test SAM on 10 sample images
   • Compare SAM predictions vs ground truth
   • Adjust SAM parameters for best results
   • Document SAM performance metrics

7. Generate Initial Predictions

   • Use SAM to generate masks for all images
   • Save to dice/annotations/predictions/
   • Calculate Dice scores against ground truth
   • Identify patterns in SAM failures

Phase 4: Analysis & Validation (Week 6)

8. Calculate Comprehensive Metrics

   • Run batch Dice calculation on all mask pairs
   • Generate statistical reports (mean, std, distribution)
   • Create visualizations (overlays, comparison grids)
   • Save results to dice/results/

9. Quality Assessment

   • Categorize segmentations: Excellent (>0.85), Good (0.70-0.85), Needs Review (<0.70)
   • Calculate additional metrics: IoU, Precision, Recall, Hausdorff distance
   • Generate quality control report
   • Document failure modes and edge cases

Phase 5: ML Model Development (Weeks 7-10)

10. Train Segmentation Model

    • Split data: 70% train, 15% validation, 15% test
    • Choose architecture: U-Net, Attention U-Net, or nnU-Net
    • Implement data augmentation pipeline
    • Train model on ground truth annotations
    • Monitor validation Dice during training

11. Model Evaluation

    • Test on held-out test set
    • Calculate Dice, IoU, and clinical metrics
    • Compare to SAM baseline
    • Generate prediction visualizations
    • Save model checkpoints

Phase 6: Clinical Validation (Weeks 11-12)

12. Expert Review

    • Have radiologists review model predictions
    • Collect feedback on clinically acceptable performance
    • Test on external validation set (if available)
    • Document cases where model fails

13. Final Report

    • Compile all metrics and visualizations
    • Write methods section describing workflow
    • Create supplemental figures
    • Prepare manuscript or technical report

🔧 Technical Debt & Improvements

High Priority

• Add DICOM file support (many medical images are DICOM)
• Implement multi-class segmentation (consolidation types)
• Add data versioning (DVC or similar)
• Create automated testing suite

Medium Priority

• Add boundary-based metrics (Surface Dice, Normalized Surface Distance)
• Implement active learning workflow
• Add export to COCO format for model training
• Create Docker container for reproducibility

Low Priority

• Add 3D visualization support
• Implement web-based annotation tool
• Add integration with PACS systems
• Create mobile app for review

📊 Success Metrics

Annotation Phase

• Target: 100+ annotated cases
• Quality: Mean inter-rater Dice > 0.80
• Efficiency: < 5 minutes per case

ML Model Phase

• Performance: Mean Dice > 0.75 on test set
• Comparison: Better than SAM baseline
• Clinical: 90% of predictions acceptable to radiologists

Publication

• Timeline: Submit manuscript within 6 months
• Target: Radiology, European Radiology, or similar
• Impact: Tool shared publicly for research use

🐛 Known Issues

• Large images (>2048x2048) may cause memory issues in Streamlit app
• SAM requires significant GPU memory (12GB+ recommended)
• Batch processing doesn't support progress resumption
• Hausdorff distance calculation is slow for large masks

📚 Learning Resources Needed

• CVAT tutorial videos for team
• Radiologic signs of pneumonia refresher
• SAM usage best practices
• Medical image segmentation literature review
• Dice coefficient vs IoU interpretation

🤝 Team Assignments

• Radiologist 1: Lead annotator, quality control
• Radiologist 2: Second annotator, validation
• ML Engineer: Preprocessing, model development
• Data Manager: File organization, data versioning
• Project Lead: Coordination, reporting

📅 Timeline Summary

• Week 1: Setup and preprocessing
• Weeks 2-4: Ground truth annotation
• Week 5: SAM integration and testing
• Week 6: Metrics and analysis
• Weeks 7-10: ML model development
• Weeks 11-12: Clinical validation
• Month 4-6: Manuscript preparation

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Last Updated: February 6, 2026 Project Status: Phase 1 - Setup Complete Next Action: Install dependencies and test Streamlit app