# 📋 TODO List: Pneumonia Consolidation Segmentation Project

## ✅ Completed

- [x] Analyze project structure and patient data format
- [x] Create preprocessing script for consolidation enhancement
- [x] Build Streamlit app for Dice score calculation
- [x] Implement SAM integration for automatic segmentation
- [x] Create requirements.txt and documentation
- [x] 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