deepprostate 1.4.2

AI-powered medical imaging analysis for prostate MRI

DeepProstate

AI-Powered Prostate MRI Analysis Platform

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Overview

DeepProstate is a medical imaging application for prostate MRI analysis using AI-powered automatic segmentation with nnUNet v2. Built with Clean Architecture principles for reliability and maintainability.

Key Features

• 🤖 AI Segmentation: Automatic prostate gland, zonal anatomy (TZ/PZ), and csPCa detection
• 🖼️ Advanced Visualization: Multi-planar views (Axial/Sagittal/Coronal) and 3D volume rendering
• ✏️ Manual Editing: Brush tools with undo/redo for segmentation refinement
• 📊 Quantitative Analysis: Volume calculations and radiomics metrics
• 🔄 Format Support: DICOM, NIfTI, MHA, NRRD
• 🛡️ Medical Compliance: HIPAA-compliant logging and audit trails

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Installation

Requirements

• Python: 3.9+
• RAM: 8GB+ recommended
• GPU: NVIDIA GPU with CUDA (optional but highly recommended for AI inference)

From PyPI (Recommended)

pip install deepprostate

From Source

git clone https://github.com/Marquita-oss/DeepProstate.git
cd deepprostate
pip install -e .

GPU Support (Recommended for AI Analysis)

For faster AI predictions, install PyTorch with CUDA support:

# CUDA 11.8
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

# CUDA 12.1
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

Note: Without GPU, AI inference will be significantly slower (CPU-only mode).

Verify Installation

deepprostate --version

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Quick Start

Launch Application

deepprostate

Basic Workflow

1. Load AI Models

   • Click "Load AI Models Path" in AI Analysis panel
   • Select folder containing nnUNet models

2. Load Patient Data

   • Use Patient Browser panel
   • Click "Load DICOM Folder" or "Load Single File"

3. Run AI Analysis

   • Select image in Patient Browser
   • Choose analysis type (Prostate/TZ-PZ/csPCa)
   • Click "Run AI Analysis"

4. Review & Refine

   • View results in 2D/3D viewers
   • Use Manual Editing tools to refine if needed
   • Export quantitative metrics

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AI Models

DeepProstate uses nnUNet v2 for automatic segmentation:

Model	Input	Output
Prostate Gland	T2W	Complete prostate mask
Zonal Anatomy	T2W	TZ and PZ masks
csPCa Detection	T2W + ADC + HBV	Cancer lesion masks

Model Directory Structure

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

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Project Structure

deepprostate/
├── deepprostate/              # Main package
│   ├── core/                  # Domain layer
│   ├── use_cases/             # Application layer
│   ├── frameworks/            # Infrastructure layer
│   └── resources/             # UI resources
├── pyproject.toml             # Package configuration
├── requirements.txt           # Dependencies
└── README.md                  # This file

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License & Disclaimer

MIT License - See LICENSE file for details.

Medical Disclaimer

⚠️ IMPORTANT: This software is intended for research and educational purposes only.

• NOT FDA-approved medical device software
• NOT intended for clinical diagnostic use
• NOT a substitute for professional medical judgment
• Users must obtain appropriate regulatory clearance for clinical use

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Citation

If you use DeepProstate in your research:

@software{deepprostate2025,
  title={DeepProstate: AI-Powered Prostate MRI Analysis Platform},
  author={Marca Ronald, Salas Rodrigo, Ponce Sebastian, Caprile Paola, Besa Cecilia},
  year={2025},
  version={1.4.2},
  url={https://github.com/Marquita-oss/DeepProstate}
}

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Support

• Issues: GitHub Issues
• Email: [e-mail]

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Acknowledgments

• nnUNet Team: Self-configuring segmentation framework
• PyQt6: UI framework
• VTK: 3D visualization
• Medical Imaging Community: Feedback and testing

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Made with ❤️ for the Medical Imaging Community