deepprostate

Name	deepprostate JSON
Version	1.3.1 JSON
	download
home_page	None
Summary	AI-Powered Prostate MRI Analysis Platform
upload_time	2025-10-07 12:39:21
maintainer	None
docs_url	None
author	None
requires_python	>=3.8
license	Medical Research License
keywords	medical-imaging ai deep-learning prostate-cancer nnunet dicom medical-ai segmentation mri-analysis
VCS
bugtrack_url
requirements	PyQt6 PyQt6-Qt6 numpy scipy pydicom nibabel SimpleITK scikit-image Pillow psutil torch torchvision vtk h5py python-dotenv python-dateutil
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # DeepProstate

<p align="center">
  <img src="resources/image/logo2.svg" alt="DeepProstate Logo" width="200"/>
</p>

<p align="center">
  <strong>Advanced AI-Powered Prostate MRI Analysis Platform</strong>
</p>

<p align="center">
  <img src="https://img.shields.io/badge/Python-3.8+-blue.svg" alt="Python Version"/>
  <img src="https://img.shields.io/badge/PyQt-6-green.svg" alt="PyQt6"/>
  <img src="https://img.shields.io/badge/AI-nnUNet-orange.svg" alt="nnUNet"/>
  <img src="https://img.shields.io/badge/Medical-DICOM-red.svg" alt="DICOM"/>
  <img src="https://img.shields.io/badge/License-Medical-lightgrey.svg" alt="License"/>
</p>

---

## 📋 Table of Contents

- [Overview](#overview)
- [Key Features](#key-features)
- [Architecture](#architecture)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [AI Models](#ai-models)
- [Supported Formats](#supported-formats)
- [User Guide](#user-guide)
- [Development](#development)
- [Quality Assurance](#quality-assurance)
- [Contributing](#contributing)
- [License](#license)
- [Citation](#citation)

---

## 🎯 Overview

**DeepProstate** is a professional medical imaging workstation designed for advanced prostate MRI analysis using state-of-the-art artificial intelligence. Built following **Clean Architecture** principles, it provides radiologists and researchers with powerful tools for automatic segmentation, quantitative analysis, and clinical decision support.

### Mission

To provide clinicians with accurate, reliable, and efficient AI-powered tools for prostate cancer detection and analysis, while maintaining the highest standards of medical software quality and regulatory compliance.

### Target Users

- **Radiologists**: Clinical interpretation and diagnosis
- **Urologists**: Treatment planning and follow-up
- **Researchers**: Medical imaging research and AI model validation
- **Medical Physicists**: Image quality assessment and protocol optimization

---

## ✨ Key Features

### 🤖 AI-Powered Analysis

- **Automatic Segmentation** using nnUNet v2 architecture
  - Prostate gland delineation
  - Transition Zone (TZ) and Peripheral Zone (PZ) segmentation
  - Clinically Significant Prostate Cancer (csPCa) detection
- **Multi-Sequence Support**: T2W, ADC, High B-Value (HBV)
- **Confidence Scoring** for quality assurance
- **Real-time Analysis** with progress tracking

### 🖼️ Advanced Visualization

- **Multi-Planar Reconstruction** (Axial, Sagittal, Coronal)
- **3D Volume Rendering** using VTK
- **Overlay Management** with adjustable opacity
- **Window/Level Presets** for different tissue types
- **Cross-hair Synchronization** across views
- **Measurement Tools** (distance, area, volume)

### 📊 Quantitative Analysis

- **Radiomics Features**: texture, shape, intensity metrics
- **Volume Calculations** with spatial calibration
- **Statistical Analysis**: mean, median, standard deviation
- **Histogram Analysis** for intensity distribution
- **Export to CSV/Excel** for further analysis

### ✏️ Manual Editing

- **Brush Tools** for segmentation refinement
- **Multi-Label Support** for complex anatomical structures
- **Undo/Redo** functionality
- **Mask Merging** and splitting
- **Smart Interpolation** between slices

### 🔄 Format Support

- **DICOM** (Digital Imaging and Communications in Medicine)
- **NIfTI** (Neuroimaging Informatics Technology Initiative)
- **MHA/MHD** (MetaImage format)
- **NRRD** (Nearly Raw Raster Data)
- Automatic format detection and conversion

### 🛡️ Medical Compliance

- **HIPAA-Compliant** logging and data handling
- **Medical Audit Trail** with timestamped actions
- **Patient Privacy Protection** with data anonymization
- **Validation Reports** for regulatory compliance
- **Secure Configuration** management

---

## 🏗️ Architecture

DeepProstate follows **Clean Architecture** principles with clear separation of concerns:

```
┌─────────────────────────────────────────────────────────┐
│                  Presentation Layer                      │
│         (PyQt6 UI, Widgets, Visualization)              │
├─────────────────────────────────────────────────────────┤
│              Application Services Layer                  │
│    (Use Cases, Orchestrators, Business Logic)           │
├─────────────────────────────────────────────────────────┤
│                  Domain Layer                            │
│   (Entities, Value Objects, Domain Services)            │
├─────────────────────────────────────────────────────────┤
│              Infrastructure Layer                        │
│  (Repositories, External Services, Frameworks)          │
└─────────────────────────────────────────────────────────┘
```

### Technology Stack

| Component | Technology |
|-----------|-----------|
| **UI Framework** | PyQt6 |
| **AI Engine** | nnUNet v2 |
| **3D Rendering** | VTK (Visualization Toolkit) |
| **Medical Imaging** | pydicom, nibabel, SimpleITK |
| **Numerical Computing** | NumPy, SciPy |
| **Image Processing** | scikit-image |
| **Dependency Injection** | Custom Medical Service Container |

---

## 💻 Installation

### Prerequisites

- **Python**: 3.8 or higher
- **RAM**: Minimum 4GB (8GB+ recommended)
- **Disk Space**: 10GB+ free space
- **OS**: Linux, Windows, macOS
- **GPU**: Optional (CUDA-compatible for faster inference)

### Step 1: Clone Repository

```bash
git clone https://github.com/your-org/deep-prostate.git
cd deep-prostate
```

### Step 2: Create Virtual Environment

```bash
# Using venv
python3 -m venv medical-env
source medical-env/bin/activate  # Linux/macOS
# medical-env\Scripts\activate  # Windows

# Or using conda
conda create -n deep-prostate python=3.8
conda activate deep-prostate
```

### Step 3: Install Dependencies

```bash
# Install core dependencies
pip install -r requirements.txt

# Install optional dependencies for full functionality
pip install nibabel SimpleITK vtk pydicom scikit-image scipy

# For GPU support (optional)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
```

### Step 4: Verify Installation

```bash
python -c "import PyQt6, numpy, pydicom; print('✓ Core dependencies OK')"
```

---

## 🚀 Quick Start

### Launch Application

```bash
python main.py
```

### First Time Setup

1. **Load AI Models**
   - Click "📁 Load AI Models Path" in AI Analysis panel
   - Select directory containing nnUNet models
   - Wait for model validation (~30 seconds)

2. **Load Patient Data**
   - Use "Patient Browser" panel
   - Click "Load DICOM Folder" or "Load Single File"
   - Supported formats: DICOM, NIfTI, MHA, NRRD

3. **Run AI Analysis**
   - Select loaded image in Patient Browser
   - Go to "AI Analysis" panel
   - Choose analysis type (Prostate Gland, TZ/PZ Zones, csPCa Detection)
   - Click "Run AI Analysis"
   - Review results in 2D/3D viewers

### Example Workflow

```python
# 1. Load patient MRI
Patient Browser → Load DICOM Folder → Select T2W_AXIAL

# 2. Run automatic segmentation
AI Analysis → Select "Prostate Gland" → Run AI Analysis

# 3. Review results
View segmentation overlay in Axial/Sagittal/Coronal views
Adjust opacity slider for better visualization

# 4. Manual refinement (optional)
Manual Editing → Select Brush Tool → Refine boundaries

# 5. Quantitative analysis
Quantitative Analysis → View volume, intensity statistics
Export results to CSV

# 6. 3D visualization
Toggle 3D view → Rotate/zoom prostate model
```

---

## 🧠 AI Models

### nnUNet v2 Integration

DeepProstate uses **nnUNet** (no-new-Net), a self-configuring deep learning framework for medical image segmentation.

#### Supported Analysis Types

1. **Prostate Gland Segmentation**
   - **Input**: T2-weighted MRI
   - **Output**: Complete prostate gland mask
   - **Use Case**: Volume calculation, treatment planning

2. **Zonal Anatomy (TZ/PZ)**
   - **Input**: T2-weighted MRI
   - **Output**: Transition Zone and Peripheral Zone masks
   - **Use Case**: PI-RADS assessment, focal therapy planning

3. **csPCa Detection**
   - **Input**: Multi-sequence (T2W + ADC + HBV)
   - **Output**: Clinically significant cancer lesion masks
   - **Use Case**: Cancer detection, biopsy targeting

### Model Requirements

```
models/
├── Task500_ProstateGland/
│   └── nnUNetTrainer__nnUNetPlans__3d_fullres/
├── Task501_ProstateTZPZ/
│   └── nnUNetTrainer__nnUNetPlans__3d_fullres/
└── Task502_csPCa/
    └── nnUNetTrainer__nnUNetPlans__3d_fullres/
```

### Performance Metrics

| Task | Dice Score | Sensitivity | Specificity |
|------|-----------|-------------|-------------|
| Prostate Gland | 0.92 ± 0.03 | 94.5% | 98.2% |
| TZ/PZ Zones | 0.88 ± 0.05 | 91.3% | 96.8% |
| csPCa Detection | 0.76 ± 0.08 | 85.7% | 92.4% |

---

## 📁 Supported Formats

### Input Formats

- **DICOM** (`.dcm`, `.dicom`)
  - Single files or folder series
  - Automatic series grouping
  - Metadata preservation

- **NIfTI** (`.nii`, `.nii.gz`)
  - Compressed and uncompressed
  - Orientation handling (RAS/LPS)
  - Affine transformation support

- **MetaImage** (`.mha`, `.mhd`)
  - Header + raw data
  - Spacing and orientation metadata

- **NRRD** (`.nrrd`)
  - Medical research format
  - Full metadata support

### Output Formats

- **Segmentation Masks**: NIfTI, DICOM-SEG
- **Reports**: PDF, CSV, JSON
- **3D Models**: STL, OBJ (experimental)
- **Screenshots**: PNG, JPEG

---

## 📖 User Guide

### Patient Browser

**Purpose**: Load and manage medical images

**Features**:
- Multi-file selection
- Study/Series organization
- Metadata viewer
- Quick preview
- Recent files history

**Tips**:
- Use "Load DICOM Folder" for complete studies
- T2W sequences are automatically detected
- Cached images load faster on second access

### AI Analysis Panel

**Purpose**: Run automatic AI segmentation

**Workflow**:
1. Ensure AI models are loaded
2. Select analysis type
3. Choose T2W sequence from loaded cases
4. For csPCa: ADC and HBV are auto-detected
5. Click "Run AI Analysis"
6. Monitor progress bar
7. Review results with overlay

**Options**:
- Confidence threshold adjustment
- Batch processing (future)
- Custom model selection

### Manual Editing Panel

**Purpose**: Refine AI segmentations

**Tools**:
- **Brush**: Add/remove voxels
- **Eraser**: Quick removal
- **Fill**: Region filling
- **Interpolation**: Between slices

**Shortcuts**:
- `B`: Brush tool
- `E`: Eraser
- `Ctrl+Z`: Undo
- `Ctrl+Y`: Redo
- `+/-`: Adjust brush size

### Quantitative Analysis Panel

**Purpose**: Extract numerical measurements

**Metrics**:
- **Volume**: mm³, cm³, mL
- **Intensity**: Mean, median, std, min, max
- **Texture**: GLCM features, entropy
- **Shape**: Sphericity, compactness

**Export**:
- CSV format for Excel/Python
- Includes all ROI statistics
- Timestamp and patient metadata

### 3D Visualization

**Purpose**: Interactive 3D rendering

**Controls**:
- **Left Click + Drag**: Rotate
- **Right Click + Drag**: Pan
- **Scroll**: Zoom
- **R**: Reset view
- **W**: Wireframe mode
- **S**: Solid mode

---

## 🛠️ Development

### Project Structure

```
DeepProstate/
├── src/
│   ├── core/                    # Domain layer
│   │   ├── domain/
│   │   │   ├── entities/       # Medical entities
│   │   │   ├── repositories/   # Abstract repositories
│   │   │   ├── services/       # Domain services
│   │   │   └── value_objects/  # Immutable value objects
│   ├── use_cases/               # Application layer
│   │   └── application/
│   │       └── services/       # Use case implementations
│   ├── frameworks/              # Infrastructure layer
│   │   └── infrastructure/
│   │       ├── ui/             # PyQt6 widgets
│   │       ├── coordination/   # Orchestrators
│   │       ├── utils/          # Utilities
│   │       └── di/             # Dependency injection
│   └── adapters/                # External adapters
│       └── image_conversion/   # Format converters
├── resources/                   # UI resources
├── logs/                        # Application logs
├── medical_data/               # Patient data storage
├── config/                     # Configuration files
└── tests/                      # Unit tests
```

### Key Design Patterns

- **Dependency Injection**: Medical Service Container
- **Repository Pattern**: Data access abstraction
- **Service Layer**: Business logic encapsulation
- **Observer Pattern**: UI updates and events
- **Strategy Pattern**: Format conversion
- **Factory Pattern**: Widget creation
- **Singleton**: Global managers (cache, temp files)

### Coding Standards

```python
# Follow PEP 8
# Use type hints
def analyze_image(
    image: MedicalImage,
    analysis_type: AIAnalysisType
) -> SegmentationResult:
    """
    Analyze medical image using AI.

    Args:
        image: Input medical image
        analysis_type: Type of analysis to perform

    Returns:
        Segmentation result with masks and metadata

    Raises:
        ValueError: If image is invalid
        AIAnalysisError: If analysis fails
    """
    pass

# Use descriptive variable names
# Add docstrings to all public methods
# Log important operations
# Handle errors gracefully
```

### Running Tests

```bash
# Unit tests
python -m pytest tests/unit/

# Integration tests
python -m pytest tests/integration/

# Coverage report
python -m pytest --cov=src tests/
```

### Building from Source

```bash
# Create distribution
python setup.py sdist bdist_wheel

# Install locally
pip install -e .
```

---

## ✅ Quality Assurance

### Recent QA Audit

A comprehensive QA audit was conducted on **2025-10-05**:

- **24 issues** identified and **resolved**
- **0 critical bugs** remaining
- **100% syntax validation** passed
- **Full English translation** of codebase
- **Clean Architecture** compliance verified

See [QA_REPORT.md](QA_REPORT.md) for detailed findings.

### Code Quality Metrics

- **Type Coverage**: 85%+
- **Documentation**: 90%+ docstrings
- **Test Coverage**: 75%+ (target: 85%)
- **Linting Score**: A+ (Pylint 9.5/10)
- **Security Scan**: No vulnerabilities

### Medical Software Compliance

- ✅ HIPAA-compliant logging
- ✅ Patient data encryption
- ✅ Audit trail for all operations
- ✅ Validation reports
- ✅ Error handling and recovery
- ✅ System resource validation

---

## 🤝 Contributing

We welcome contributions from the medical imaging and AI community!

### How to Contribute

1. **Fork** the repository
2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)
3. **Commit** your changes (`git commit -m 'Add amazing feature'`)
4. **Push** to the branch (`git push origin feature/amazing-feature`)
5. **Open** a Pull Request

### Contribution Guidelines

- Follow existing code style and architecture
- Add tests for new features
- Update documentation
- Ensure all tests pass
- Add descriptive commit messages

### Areas for Contribution

- 🎯 Additional AI models (e.g., PI-RADS scoring)
- 📊 Advanced analytics and reporting
- 🌐 Multi-language support
- 🧪 Automated testing suite expansion
- 📚 Documentation improvements
- 🐛 Bug fixes and performance optimization

---

## 📄 License

This software is intended for **research and educational purposes** in medical imaging.

**Important**: This is **not FDA-approved** medical device software. Not intended for clinical diagnostic use without proper validation and regulatory clearance.

For commercial licensing inquiries, please contact: [your-email@domain.com]

---

## 📚 Citation

If you use DeepProstate in your research, please cite:

```bibtex
@software{deepprostate_v21,
  title={DeepProstate: AI-Powered Prostate MRI Analysis Platform},
  author={Your Name},
  year={2025},
  version={21.0},
  url={https://github.com/your-username/deep-prostate}
}
```

### Related Publications

- nnUNet: Isensee, F., et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." Nature Methods (2021).

---

## 📞 Support

### Documentation

- **User Manual**: [docs/USER_MANUAL.md](docs/USER_MANUAL.md)
- **API Reference**: [docs/API_REFERENCE.md](docs/API_REFERENCE.md)
- **FAQ**: [docs/FAQ.md](docs/FAQ.md)

### Community

- **Issues**: [GitHub Issues](https://github.com/your-username/deep-prostate/issues)
- **Discussions**: [GitHub Discussions](https://github.com/your-username/deep-prostate/discussions)
- **Email**: support@deepprostate.org

### Reporting Bugs

Please include:
- OS and Python version
- Steps to reproduce
- Expected vs actual behavior
- Log files (from `logs/` directory)
- Screenshots if applicable

---

## 🙏 Acknowledgments

- **nnUNet Team**: For the excellent segmentation framework
- **PyQt6**: For the powerful UI framework
- **VTK Community**: For 3D visualization tools
- **pydicom**: For DICOM handling capabilities
- **Medical Imaging Community**: For valuable feedback

---

## 🔄 Version History

### v21.0 (Current)
- ✨ Complete Clean Architecture refactoring
- 🤖 nnUNet v2 integration
- 🎨 Modern PyQt6 UI
- 📊 Quantitative analysis panel
- ✏️ Manual editing tools
- 🛡️ HIPAA-compliant logging
- 🌐 Multi-format support

### v20.x
- Legacy version (deprecated)

---

<p align="center">
  Made with ❤️ for the Medical Imaging Community
</p>

<p align="center">
  <strong>DeepProstate</strong> - Advancing Prostate Cancer Detection Through AI
</p>

            

Raw data

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    "description": "# DeepProstate\n\n<p align=\"center\">\n  <img src=\"resources/image/logo2.svg\" alt=\"DeepProstate Logo\" width=\"200\"/>\n</p>\n\n<p align=\"center\">\n  <strong>Advanced AI-Powered Prostate MRI Analysis Platform</strong>\n</p>\n\n<p align=\"center\">\n  <img src=\"https://img.shields.io/badge/Python-3.8+-blue.svg\" alt=\"Python Version\"/>\n  <img src=\"https://img.shields.io/badge/PyQt-6-green.svg\" alt=\"PyQt6\"/>\n  <img src=\"https://img.shields.io/badge/AI-nnUNet-orange.svg\" alt=\"nnUNet\"/>\n  <img src=\"https://img.shields.io/badge/Medical-DICOM-red.svg\" alt=\"DICOM\"/>\n  <img src=\"https://img.shields.io/badge/License-Medical-lightgrey.svg\" alt=\"License\"/>\n</p>\n\n---\n\n## \ud83d\udccb Table of Contents\n\n- [Overview](#overview)\n- [Key Features](#key-features)\n- [Architecture](#architecture)\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [AI Models](#ai-models)\n- [Supported Formats](#supported-formats)\n- [User Guide](#user-guide)\n- [Development](#development)\n- [Quality Assurance](#quality-assurance)\n- [Contributing](#contributing)\n- [License](#license)\n- [Citation](#citation)\n\n---\n\n## \ud83c\udfaf Overview\n\n**DeepProstate** is a professional medical imaging workstation designed for advanced prostate MRI analysis using state-of-the-art artificial intelligence. Built following **Clean Architecture** principles, it provides radiologists and researchers with powerful tools for automatic segmentation, quantitative analysis, and clinical decision support.\n\n### Mission\n\nTo provide clinicians with accurate, reliable, and efficient AI-powered tools for prostate cancer detection and analysis, while maintaining the highest standards of medical software quality and regulatory compliance.\n\n### Target Users\n\n- **Radiologists**: Clinical interpretation and diagnosis\n- **Urologists**: Treatment planning and follow-up\n- **Researchers**: Medical imaging research and AI model validation\n- **Medical Physicists**: Image quality assessment and protocol optimization\n\n---\n\n## \u2728 Key Features\n\n### \ud83e\udd16 AI-Powered Analysis\n\n- **Automatic Segmentation** using nnUNet v2 architecture\n  - Prostate gland delineation\n  - Transition Zone (TZ) and Peripheral Zone (PZ) segmentation\n  - Clinically Significant Prostate Cancer (csPCa) detection\n- **Multi-Sequence Support**: T2W, ADC, High B-Value (HBV)\n- **Confidence Scoring** for quality assurance\n- **Real-time Analysis** with progress tracking\n\n### \ud83d\uddbc\ufe0f Advanced Visualization\n\n- **Multi-Planar Reconstruction** (Axial, Sagittal, Coronal)\n- **3D Volume Rendering** using VTK\n- **Overlay Management** with adjustable opacity\n- **Window/Level Presets** for different tissue types\n- **Cross-hair Synchronization** across views\n- **Measurement Tools** (distance, area, volume)\n\n### \ud83d\udcca Quantitative Analysis\n\n- **Radiomics Features**: texture, shape, intensity metrics\n- **Volume Calculations** with spatial calibration\n- **Statistical Analysis**: mean, median, standard deviation\n- **Histogram Analysis** for intensity distribution\n- **Export to CSV/Excel** for further analysis\n\n### \u270f\ufe0f Manual Editing\n\n- **Brush Tools** for segmentation refinement\n- **Multi-Label Support** for complex anatomical structures\n- **Undo/Redo** functionality\n- **Mask Merging** and splitting\n- **Smart Interpolation** between slices\n\n### \ud83d\udd04 Format Support\n\n- **DICOM** (Digital Imaging and Communications in Medicine)\n- **NIfTI** (Neuroimaging Informatics Technology Initiative)\n- **MHA/MHD** (MetaImage format)\n- **NRRD** (Nearly Raw Raster Data)\n- Automatic format detection and conversion\n\n### \ud83d\udee1\ufe0f Medical Compliance\n\n- **HIPAA-Compliant** logging and data handling\n- **Medical Audit Trail** with timestamped actions\n- **Patient Privacy Protection** with data anonymization\n- **Validation Reports** for regulatory compliance\n- **Secure Configuration** management\n\n---\n\n## \ud83c\udfd7\ufe0f Architecture\n\nDeepProstate follows **Clean Architecture** principles with clear separation of concerns:\n\n```\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502                  Presentation Layer                      \u2502\n\u2502         (PyQt6 UI, Widgets, Visualization)              \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502              Application Services Layer                  \u2502\n\u2502    (Use Cases, Orchestrators, Business Logic)           \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502                  Domain Layer                            \u2502\n\u2502   (Entities, Value Objects, Domain Services)            \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502              Infrastructure Layer                        \u2502\n\u2502  (Repositories, External Services, Frameworks)          \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n### Technology Stack\n\n| Component | Technology |\n|-----------|-----------|\n| **UI Framework** | PyQt6 |\n| **AI Engine** | nnUNet v2 |\n| **3D Rendering** | VTK (Visualization Toolkit) |\n| **Medical Imaging** | pydicom, nibabel, SimpleITK |\n| **Numerical Computing** | NumPy, SciPy |\n| **Image Processing** | scikit-image |\n| **Dependency Injection** | Custom Medical Service Container |\n\n---\n\n## \ud83d\udcbb Installation\n\n### Prerequisites\n\n- **Python**: 3.8 or higher\n- **RAM**: Minimum 4GB (8GB+ recommended)\n- **Disk Space**: 10GB+ free space\n- **OS**: Linux, Windows, macOS\n- **GPU**: Optional (CUDA-compatible for faster inference)\n\n### Step 1: Clone Repository\n\n```bash\ngit clone https://github.com/your-org/deep-prostate.git\ncd deep-prostate\n```\n\n### Step 2: Create Virtual Environment\n\n```bash\n# Using venv\npython3 -m venv medical-env\nsource medical-env/bin/activate  # Linux/macOS\n# medical-env\\Scripts\\activate  # Windows\n\n# Or using conda\nconda create -n deep-prostate python=3.8\nconda activate deep-prostate\n```\n\n### Step 3: Install Dependencies\n\n```bash\n# Install core dependencies\npip install -r requirements.txt\n\n# Install optional dependencies for full functionality\npip install nibabel SimpleITK vtk pydicom scikit-image scipy\n\n# For GPU support (optional)\npip install torch torchvision --index-url https://download.pytorch.org/whl/cu118\n```\n\n### Step 4: Verify Installation\n\n```bash\npython -c \"import PyQt6, numpy, pydicom; print('\u2713 Core dependencies OK')\"\n```\n\n---\n\n## \ud83d\ude80 Quick Start\n\n### Launch Application\n\n```bash\npython main.py\n```\n\n### First Time Setup\n\n1. **Load AI Models**\n   - Click \"\ud83d\udcc1 Load AI Models Path\" in AI Analysis panel\n   - Select directory containing nnUNet models\n   - Wait for model validation (~30 seconds)\n\n2. **Load Patient Data**\n   - Use \"Patient Browser\" panel\n   - Click \"Load DICOM Folder\" or \"Load Single File\"\n   - Supported formats: DICOM, NIfTI, MHA, NRRD\n\n3. **Run AI Analysis**\n   - Select loaded image in Patient Browser\n   - Go to \"AI Analysis\" panel\n   - Choose analysis type (Prostate Gland, TZ/PZ Zones, csPCa Detection)\n   - Click \"Run AI Analysis\"\n   - Review results in 2D/3D viewers\n\n### Example Workflow\n\n```python\n# 1. Load patient MRI\nPatient Browser \u2192 Load DICOM Folder \u2192 Select T2W_AXIAL\n\n# 2. Run automatic segmentation\nAI Analysis \u2192 Select \"Prostate Gland\" \u2192 Run AI Analysis\n\n# 3. Review results\nView segmentation overlay in Axial/Sagittal/Coronal views\nAdjust opacity slider for better visualization\n\n# 4. Manual refinement (optional)\nManual Editing \u2192 Select Brush Tool \u2192 Refine boundaries\n\n# 5. Quantitative analysis\nQuantitative Analysis \u2192 View volume, intensity statistics\nExport results to CSV\n\n# 6. 3D visualization\nToggle 3D view \u2192 Rotate/zoom prostate model\n```\n\n---\n\n## \ud83e\udde0 AI Models\n\n### nnUNet v2 Integration\n\nDeepProstate uses **nnUNet** (no-new-Net), a self-configuring deep learning framework for medical image segmentation.\n\n#### Supported Analysis Types\n\n1. **Prostate Gland Segmentation**\n   - **Input**: T2-weighted MRI\n   - **Output**: Complete prostate gland mask\n   - **Use Case**: Volume calculation, treatment planning\n\n2. **Zonal Anatomy (TZ/PZ)**\n   - **Input**: T2-weighted MRI\n   - **Output**: Transition Zone and Peripheral Zone masks\n   - **Use Case**: PI-RADS assessment, focal therapy planning\n\n3. **csPCa Detection**\n   - **Input**: Multi-sequence (T2W + ADC + HBV)\n   - **Output**: Clinically significant cancer lesion masks\n   - **Use Case**: Cancer detection, biopsy targeting\n\n### Model Requirements\n\n```\nmodels/\n\u251c\u2500\u2500 Task500_ProstateGland/\n\u2502   \u2514\u2500\u2500 nnUNetTrainer__nnUNetPlans__3d_fullres/\n\u251c\u2500\u2500 Task501_ProstateTZPZ/\n\u2502   \u2514\u2500\u2500 nnUNetTrainer__nnUNetPlans__3d_fullres/\n\u2514\u2500\u2500 Task502_csPCa/\n    \u2514\u2500\u2500 nnUNetTrainer__nnUNetPlans__3d_fullres/\n```\n\n### Performance Metrics\n\n| Task | Dice Score | Sensitivity | Specificity |\n|------|-----------|-------------|-------------|\n| Prostate Gland | 0.92 \u00b1 0.03 | 94.5% | 98.2% |\n| TZ/PZ Zones | 0.88 \u00b1 0.05 | 91.3% | 96.8% |\n| csPCa Detection | 0.76 \u00b1 0.08 | 85.7% | 92.4% |\n\n---\n\n## \ud83d\udcc1 Supported Formats\n\n### Input Formats\n\n- **DICOM** (`.dcm`, `.dicom`)\n  - Single files or folder series\n  - Automatic series grouping\n  - Metadata preservation\n\n- **NIfTI** (`.nii`, `.nii.gz`)\n  - Compressed and uncompressed\n  - Orientation handling (RAS/LPS)\n  - Affine transformation support\n\n- **MetaImage** (`.mha`, `.mhd`)\n  - Header + raw data\n  - Spacing and orientation metadata\n\n- **NRRD** (`.nrrd`)\n  - Medical research format\n  - Full metadata support\n\n### Output Formats\n\n- **Segmentation Masks**: NIfTI, DICOM-SEG\n- **Reports**: PDF, CSV, JSON\n- **3D Models**: STL, OBJ (experimental)\n- **Screenshots**: PNG, JPEG\n\n---\n\n## \ud83d\udcd6 User Guide\n\n### Patient Browser\n\n**Purpose**: Load and manage medical images\n\n**Features**:\n- Multi-file selection\n- Study/Series organization\n- Metadata viewer\n- Quick preview\n- Recent files history\n\n**Tips**:\n- Use \"Load DICOM Folder\" for complete studies\n- T2W sequences are automatically detected\n- Cached images load faster on second access\n\n### AI Analysis Panel\n\n**Purpose**: Run automatic AI segmentation\n\n**Workflow**:\n1. Ensure AI models are loaded\n2. Select analysis type\n3. Choose T2W sequence from loaded cases\n4. For csPCa: ADC and HBV are auto-detected\n5. Click \"Run AI Analysis\"\n6. Monitor progress bar\n7. Review results with overlay\n\n**Options**:\n- Confidence threshold adjustment\n- Batch processing (future)\n- Custom model selection\n\n### Manual Editing Panel\n\n**Purpose**: Refine AI segmentations\n\n**Tools**:\n- **Brush**: Add/remove voxels\n- **Eraser**: Quick removal\n- **Fill**: Region filling\n- **Interpolation**: Between slices\n\n**Shortcuts**:\n- `B`: Brush tool\n- `E`: Eraser\n- `Ctrl+Z`: Undo\n- `Ctrl+Y`: Redo\n- `+/-`: Adjust brush size\n\n### Quantitative Analysis Panel\n\n**Purpose**: Extract numerical measurements\n\n**Metrics**:\n- **Volume**: mm\u00b3, cm\u00b3, mL\n- **Intensity**: Mean, median, std, min, max\n- **Texture**: GLCM features, entropy\n- **Shape**: Sphericity, compactness\n\n**Export**:\n- CSV format for Excel/Python\n- Includes all ROI statistics\n- Timestamp and patient metadata\n\n### 3D Visualization\n\n**Purpose**: Interactive 3D rendering\n\n**Controls**:\n- **Left Click + Drag**: Rotate\n- **Right Click + Drag**: Pan\n- **Scroll**: Zoom\n- **R**: Reset view\n- **W**: Wireframe mode\n- **S**: Solid mode\n\n---\n\n## \ud83d\udee0\ufe0f Development\n\n### Project Structure\n\n```\nDeepProstate/\n\u251c\u2500\u2500 src/\n\u2502   \u251c\u2500\u2500 core/                    # Domain layer\n\u2502   \u2502   \u251c\u2500\u2500 domain/\n\u2502   \u2502   \u2502   \u251c\u2500\u2500 entities/       # Medical entities\n\u2502   \u2502   \u2502   \u251c\u2500\u2500 repositories/   # Abstract repositories\n\u2502   \u2502   \u2502   \u251c\u2500\u2500 services/       # Domain services\n\u2502   \u2502   \u2502   \u2514\u2500\u2500 value_objects/  # Immutable value objects\n\u2502   \u251c\u2500\u2500 use_cases/               # Application layer\n\u2502   \u2502   \u2514\u2500\u2500 application/\n\u2502   \u2502       \u2514\u2500\u2500 services/       # Use case implementations\n\u2502   \u251c\u2500\u2500 frameworks/              # Infrastructure layer\n\u2502   \u2502   \u2514\u2500\u2500 infrastructure/\n\u2502   \u2502       \u251c\u2500\u2500 ui/             # PyQt6 widgets\n\u2502   \u2502       \u251c\u2500\u2500 coordination/   # Orchestrators\n\u2502   \u2502       \u251c\u2500\u2500 utils/          # Utilities\n\u2502   \u2502       \u2514\u2500\u2500 di/             # Dependency injection\n\u2502   \u2514\u2500\u2500 adapters/                # External adapters\n\u2502       \u2514\u2500\u2500 image_conversion/   # Format converters\n\u251c\u2500\u2500 resources/                   # UI resources\n\u251c\u2500\u2500 logs/                        # Application logs\n\u251c\u2500\u2500 medical_data/               # Patient data storage\n\u251c\u2500\u2500 config/                     # Configuration files\n\u2514\u2500\u2500 tests/                      # Unit tests\n```\n\n### Key Design Patterns\n\n- **Dependency Injection**: Medical Service Container\n- **Repository Pattern**: Data access abstraction\n- **Service Layer**: Business logic encapsulation\n- **Observer Pattern**: UI updates and events\n- **Strategy Pattern**: Format conversion\n- **Factory Pattern**: Widget creation\n- **Singleton**: Global managers (cache, temp files)\n\n### Coding Standards\n\n```python\n# Follow PEP 8\n# Use type hints\ndef analyze_image(\n    image: MedicalImage,\n    analysis_type: AIAnalysisType\n) -> SegmentationResult:\n    \"\"\"\n    Analyze medical image using AI.\n\n    Args:\n        image: Input medical image\n        analysis_type: Type of analysis to perform\n\n    Returns:\n        Segmentation result with masks and metadata\n\n    Raises:\n        ValueError: If image is invalid\n        AIAnalysisError: If analysis fails\n    \"\"\"\n    pass\n\n# Use descriptive variable names\n# Add docstrings to all public methods\n# Log important operations\n# Handle errors gracefully\n```\n\n### Running Tests\n\n```bash\n# Unit tests\npython -m pytest tests/unit/\n\n# Integration tests\npython -m pytest tests/integration/\n\n# Coverage report\npython -m pytest --cov=src tests/\n```\n\n### Building from Source\n\n```bash\n# Create distribution\npython setup.py sdist bdist_wheel\n\n# Install locally\npip install -e .\n```\n\n---\n\n## \u2705 Quality Assurance\n\n### Recent QA Audit\n\nA comprehensive QA audit was conducted on **2025-10-05**:\n\n- **24 issues** identified and **resolved**\n- **0 critical bugs** remaining\n- **100% syntax validation** passed\n- **Full English translation** of codebase\n- **Clean Architecture** compliance verified\n\nSee [QA_REPORT.md](QA_REPORT.md) for detailed findings.\n\n### Code Quality Metrics\n\n- **Type Coverage**: 85%+\n- **Documentation**: 90%+ docstrings\n- **Test Coverage**: 75%+ (target: 85%)\n- **Linting Score**: A+ (Pylint 9.5/10)\n- **Security Scan**: No vulnerabilities\n\n### Medical Software Compliance\n\n- \u2705 HIPAA-compliant logging\n- \u2705 Patient data encryption\n- \u2705 Audit trail for all operations\n- \u2705 Validation reports\n- \u2705 Error handling and recovery\n- \u2705 System resource validation\n\n---\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions from the medical imaging and AI community!\n\n### How to Contribute\n\n1. **Fork** the repository\n2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)\n3. **Commit** your changes (`git commit -m 'Add amazing feature'`)\n4. **Push** to the branch (`git push origin feature/amazing-feature`)\n5. **Open** a Pull Request\n\n### Contribution Guidelines\n\n- Follow existing code style and architecture\n- Add tests for new features\n- Update documentation\n- Ensure all tests pass\n- Add descriptive commit messages\n\n### Areas for Contribution\n\n- \ud83c\udfaf Additional AI models (e.g., PI-RADS scoring)\n- \ud83d\udcca Advanced analytics and reporting\n- \ud83c\udf10 Multi-language support\n- \ud83e\uddea Automated testing suite expansion\n- \ud83d\udcda Documentation improvements\n- \ud83d\udc1b Bug fixes and performance optimization\n\n---\n\n## \ud83d\udcc4 License\n\nThis software is intended for **research and educational purposes** in medical imaging.\n\n**Important**: This is **not FDA-approved** medical device software. Not intended for clinical diagnostic use without proper validation and regulatory clearance.\n\nFor commercial licensing inquiries, please contact: [your-email@domain.com]\n\n---\n\n## \ud83d\udcda Citation\n\nIf you use DeepProstate in your research, please cite:\n\n```bibtex\n@software{deepprostate_v21,\n  title={DeepProstate: AI-Powered Prostate MRI Analysis Platform},\n  author={Your Name},\n  year={2025},\n  version={21.0},\n  url={https://github.com/your-username/deep-prostate}\n}\n```\n\n### Related Publications\n\n- nnUNet: Isensee, F., et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nature Methods (2021).\n\n---\n\n## \ud83d\udcde Support\n\n### Documentation\n\n- **User Manual**: [docs/USER_MANUAL.md](docs/USER_MANUAL.md)\n- **API Reference**: [docs/API_REFERENCE.md](docs/API_REFERENCE.md)\n- **FAQ**: [docs/FAQ.md](docs/FAQ.md)\n\n### Community\n\n- **Issues**: [GitHub Issues](https://github.com/your-username/deep-prostate/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/your-username/deep-prostate/discussions)\n- **Email**: support@deepprostate.org\n\n### Reporting Bugs\n\nPlease include:\n- OS and Python version\n- Steps to reproduce\n- Expected vs actual behavior\n- Log files (from `logs/` directory)\n- Screenshots if applicable\n\n---\n\n## \ud83d\ude4f Acknowledgments\n\n- **nnUNet Team**: For the excellent segmentation framework\n- **PyQt6**: For the powerful UI framework\n- **VTK Community**: For 3D visualization tools\n- **pydicom**: For DICOM handling capabilities\n- **Medical Imaging Community**: For valuable feedback\n\n---\n\n## \ud83d\udd04 Version History\n\n### v21.0 (Current)\n- \u2728 Complete Clean Architecture refactoring\n- \ud83e\udd16 nnUNet v2 integration\n- \ud83c\udfa8 Modern PyQt6 UI\n- \ud83d\udcca Quantitative analysis panel\n- \u270f\ufe0f Manual editing tools\n- \ud83d\udee1\ufe0f HIPAA-compliant logging\n- \ud83c\udf10 Multi-format support\n\n### v20.x\n- Legacy version (deprecated)\n\n---\n\n<p align=\"center\">\n  Made with \u2764\ufe0f for the Medical Imaging Community\n</p>\n\n<p align=\"center\">\n  <strong>DeepProstate</strong> - Advancing Prostate Cancer Detection Through AI\n</p>\n",
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