Epistemic Uncertainty Modeling for Vessel Segmentation

Area of Science:

• Medical Imaging
• Cardiovascular Interventions
• Artificial Intelligence in Medicine

Background:

• X-ray angiograms are the standard for cardiovascular interventions but are challenging to analyze due to contrast issues and artifacts.
• Accurate multi-artery segmentation is crucial for improving cardiologist analysis and enabling advanced applications like 3D reconstruction and motion tracking.

Purpose of the Study:

• To develop a general and accurate method for multi-artery segmentation in X-ray angiograms.
• To provide uncertainty quantification for the segmentation to guide expert review and data augmentation.

Main Methods:

• A novel multi-artery segmentation method was developed and validated on clinical X-ray angiogram data.
• The method incorporates epistemic uncertainty mapping to identify areas requiring expert validation.

Main Results:

• The proposed method achieved an average segmentation accuracy of 94.9% on clinical data.
• Demonstrated successful transfer learning across different artery types.
• Epistemic uncertainty maps effectively highlighted regions needing expert review.

Conclusions:

• The developed segmentation method significantly improves the analysis of X-ray angiograms.
• Uncertainty maps are valuable for ensuring segmentation reliability and identifying areas for further data improvement.