The LibreHealth Radiology Artifact Detection project aims to create an intelligent system that can identify and annotate similar image artifacts across multiple radiological studies based on user-selected regions of interest (ROI). This tool will enhance radiologist workflow by automatically detecting and marking similar artifacts across an entire worklist, reducing manual annotation time and improving consistency in artifact identification.
Core Functionality:
ROI Selection and Analysis
- Interactive region selection tools
- Feature extraction from selected artifacts
- Artifact characteristic profiling
- Annotation metadata storage
- Multi-slice artifact tracking
Similarity Detection
- Deep learning-based feature matching
- Artifact pattern recognition
- Cross-image similarity scoring
- Confidence level calculation
- False positive reduction
Automated Annotation
- Consistent annotation styling
- Automatic segmentation
- Annotation propagation
- Metadata synchronization
- Version control for annotations
The deliverables of the project are as follows:
- Develop an interactive ROI selection interface integrated with OHIF viewer
- Create a deep learning model for artifact similarity detection
- Implement automated annotation propagation across images
- Build a review and validation interface for radiologists
- Provide performance analytics and quality metrics
- Create comprehensive documentation and training materials
The project will significantly improve radiological workflow by automating the tedious process of identifying and annotating similar artifacts across multiple images. The intelligent system will learn from radiologist-selected examples and propagate annotations consistently, maintaining the same style and metadata across all identified instances.
The integration with the existing LibreHealth Radiology viewer ensures seamless workflow incorporation while providing powerful new capabilities for artifact management. The system’s ability to learn from user selections and improve over time makes it an invaluable tool for maintaining consistent artifact documentation across large image sets.
This project will enhance the quality of radiological analysis by ensuring consistent artifact identification and documentation while significantly reducing the time required for manual annotation. The automated system will serve as a powerful assistant to radiologists, allowing them to focus more on diagnosis and less on repetitive annotation tasks.
Project size: Large (~350 hours)
Mentors: @sunbiz and @Rohan.isaac27