# Project Accomplishments Checklist ✅

**Last Updated:** August 20, 2025

## 🎯 Overlay Visualization System - COMPLETED

### ✅ Core Visualization Components
- [x] **Multi-slice Overlay Panels** (`src/training/callbacks/visualization.py`)
  - [x] Ground truth vs prediction overlays with customizable colors (Green/Red)
  - [x] Automatic slice selection (25%, 50%, 75% depth) or manual specification
  - [x] Support for multi-class segmentation with class-specific visualization
  - [x] High-DPI PNG export (150 DPI) for publication quality
  - [x] Robust image normalization and error handling

- [x] **Probability Heatmap Generation** (`save_prob_panel`)
  - [x] Class-specific probability visualization with magma colormap
  - [x] Softmax application with configurable thresholds
  - [x] Overlay transparency control (alpha blending)
  - [x] Multi-slice probability display

### ✅ Enhanced Inference System
- [x] **Command-Line Interface** (`src/inference/inference.py`)
  - [x] **Input Flexibility**: Single files, directories, or Decathlon configs
  - [x] **Output Formats**: NIfTI masks, overlay PNGs, probability heatmaps
  - [x] **Advanced Features**: TTA (flip augmentation), AMP (mixed precision)
  - [x] **Device Support**: Auto-detection for CPU/CUDA/MPS
  - [x] **Sliding Window Inference** with configurable overlap

- [x] **Affine Preservation**: Original image geometry maintained in outputs
- [x] **Batch Processing**: Multiple cases processed efficiently
- [x] **Dice Metric Calculation**: Automatic evaluation when labels available

### ✅ Training Integration
- [x] **Validation Overlays** (`src/training/train_enhanced.py`)
  - [x] Configurable overlay generation during training validation
  - [x] Automatic overlay directory management
  - [x] Controllable overlay count (`--overlays-max`)
  - [x] Custom slice selection support
  - [x] Integration with existing training workflow

### ✅ Quality Assurance & Testing
- [x] **Unit Tests** (`tests/unit/test_visualization_panels.py`)
  - [x] Synthetic data generation for reproducible testing
  - [x] CPU-only tests for CI/CD compatibility
  - [x] Error handling and edge case coverage
  - [x] PNG file generation validation

- [x] **Integration Tests** (`tests/integration/test_inference_cli_smoke.py`)
  - [x] End-to-end CLI testing with synthetic NIfTI files
  - [x] Model checkpoint creation and loading
  - [x] Overlay and mask generation verification
  - [x] Multi-format output validation

### ✅ Documentation & Examples
- [x] **README Integration**: Complete overlay visualization section
- [x] **Command Examples**: Training and inference workflows
- [x] **API Documentation**: Function signatures and parameters
- [x] **Output Structure**: Clear directory organization explanation

### ✅ Interactive Analysis Tools
- [x] **Qualitative Review Notebook** (`notebooks/qualitative_review_task01.ipynb`)
  - [x] Training overlay examination and analysis
  - [x] Inference result visualization
  - [x] Model performance assessment tools
  - [x] Interactive overlay exploration

## 🎯 Project Organization - COMPLETED

### ✅ Root Directory Cleanup
- [x] **File Organization**: 40+ files organized into logical subdirectories
- [x] **Script Categorization**: Demo, validation, setup, utility scripts
- [x] **Documentation Structure**: Deployment, status, reference docs
- [x] **Convenience Tools**: `scripts.sh` for easy access to functionality

### ✅ Directory Structure
```
✅ docs/
  ├── deployment/     # Docker and deployment files
  ├── status/         # Status and verification files
  └── PROJECT_*.md    # Planning and roadmap documents

✅ scripts/
  ├── demo/           # Demo workflow scripts
  ├── validation/     # Test and validation scripts
  ├── setup/          # Setup and installation scripts
  └── utilities/      # Utility and helper scripts

✅ config/
  └── development/    # Development configuration files
```

## 🎯 Current System Capabilities

### ✅ Command-Line Workflows
```bash
# ✅ Validation set inference with full visualization
python src/inference/inference.py \
    --config config/recipes/unetr_multimodal.json \
    --dataset-config config/datasets/msd_task01_brain.json \
    --model models/unetr/best.pt \
    --save-overlays --save-prob-maps --tta --amp

# ✅ New image processing with custom slice selection
python src/inference/inference.py \
    --config config/recipes/unetr_multimodal.json \
    --model models/unetr/best.pt \
    --input data/new_cases \
    --save-overlays --slices 40,60,80

# ✅ Training with validation overlays
python src/training/train_enhanced.py \
    --config config/recipes/unetr_multimodal.json \
    --dataset-config config/datasets/msd_task01_brain.json \
    --save-overlays --overlays-max 5
```

### ✅ Output Structure
```
✅ Organized output directories:
reports/inference_exports/
├── masks/           # NIfTI segmentation masks (affine preserved)
├── overlays/        # Multi-slice PNG overlays (GT vs Pred)
└── prob_maps/       # Class probability heatmaps
```

## 🎯 Technical Features Implemented

### ✅ Visualization Features
- [x] **Multi-slice Overlays**: Automatic slice selection or manual specification
- [x] **Color Customization**: Configurable colormaps for GT (green) and predictions (red)
- [x] **Transparency Control**: Adjustable alpha blending for overlay visibility
- [x] **Class Selection**: Specific class visualization for multi-class problems
- [x] **High-Quality Export**: 150 DPI PNG with tight layout for publications

### ✅ Inference Features
- [x] **Test-Time Augmentation**: Flip-based TTA for improved accuracy
- [x] **Mixed Precision**: AMP support for faster inference on CUDA
- [x] **Sliding Window**: Configurable overlap for large image processing
- [x] **Device Auto-Detection**: Automatic CPU/CUDA/MPS selection
- [x] **Batch Processing**: Efficient handling of multiple cases

### ✅ Data Handling
- [x] **Affine Preservation**: Original image geometry maintained
- [x] **Format Support**: NIfTI input/output with metadata preservation
- [x] **Transform Integration**: MONAI transform pipeline compatibility
- [x] **Error Handling**: Robust error recovery and user feedback

## 🚀 Ready to Use Commands

### Immediate Testing
```bash
# ✅ Test visualization system
python -m pytest tests/unit/test_visualization_panels.py -v

# ✅ Test inference CLI
python -m pytest tests/integration/test_inference_cli_smoke.py -v

# ✅ Quick inference help
python src/inference/inference.py --help

# ✅ View organized scripts
./scripts.sh help
```

### Quality Review
```bash
# ✅ Open qualitative review notebook
jupyter notebook notebooks/qualitative_review_task01.ipynb

# ✅ Check project organization
ls -la docs/ scripts/ config/

# ✅ Verify overlay generation
python src/inference/inference.py \
    --config config/recipes/unetr_multimodal.json \
    --model models/unetr/best.pt \
    --input data/test_case.nii.gz \
    --save-overlays --slices auto
```

## 📊 Summary Statistics

### Code Organization
- ✅ **Files Organized**: 40+ files moved to logical subdirectories
- ✅ **Test Coverage**: Unit and integration tests implemented
- ✅ **Documentation**: Comprehensive README and planning docs
- ✅ **Scripts Available**: 25+ organized utility and demo scripts

### Visualization Capabilities
- ✅ **Overlay Types**: Multi-slice overlays and probability heatmaps
- ✅ **Export Formats**: High-quality PNG (150 DPI) and NIfTI
- ✅ **Customization**: Colors, transparency, slices, class selection
- ✅ **Integration**: Training and inference workflow integration

### System Robustness
- ✅ **Error Handling**: Comprehensive error recovery and validation
- ✅ **Device Support**: CPU, CUDA, MPS auto-detection
- ✅ **Performance**: AMP and TTA optimization options
- ✅ **Compatibility**: MONAI transform and model integration

---

## ✨ What This Enables

**Researchers can now:**
- ✅ Generate publication-quality overlay visualizations automatically
- ✅ Process new medical images with comprehensive overlay export
- ✅ Monitor training progress with validation overlays
- ✅ Perform qualitative model assessment with interactive tools

**Clinicians can now:**
- ✅ Visualize AI predictions overlaid on original images
- ✅ Review probability heatmaps for confidence assessment
- ✅ Export results in standard medical formats (NIfTI)
- ✅ Process multiple cases efficiently with batch inference

**System is ready for:**
- ✅ Production deployment with robust error handling
- ✅ Integration into clinical workflows
- ✅ Extension with additional model architectures
- ✅ Scaling to large-scale processing pipelines
