INTRACRANIAL VESSEL WALL SEGMENTATION FOR ATHEROSCLEROTIC PLAQUE QUANTIFICATION

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Biomedical Engineering

Background:

• Accurate intracranial vessel wall segmentation is crucial for quantifying intracranial atherosclerosis using magnetic resonance vessel wall imaging.
• Previous 2D deep learning models require improvement for enhanced geometric continuity and conformity.

Purpose of the Study:

• To develop an advanced 2.5D deep learning network for improved intracranial vessel wall segmentation.
• To enhance geometric accuracy and clinical relevance in plaque burden assessment.

Main Methods:

• Utilized a 2.5D structure to balance network complexity and geometric continuity.
• Employed a UNET++ model for improved structure adaptation.
• Incorporated an approximated Hausdorff distance (HD) loss for enhanced geometry conformality.
• Integrated the normalized wall index (NWI) for clinical endpoint matching.

Main Results:

• Achieved high Dice similarity coefficients (0.9172 ± 0.0598 for lumen, 0.7833 ± 0.0867 for vessel wall).
• Demonstrated improved Hausdorff distance and mean surface distance compared to the original 2D UNET.
• Reduced the mean absolute error in normalized wall index (NWI) from 0.0732 ± 0.0294 to 0.0725 ± 0.0333.

Conclusions:

• The proposed 2.5D deep learning network significantly improves intracranial vessel wall segmentation accuracy.
• The enhanced segmentation leads to more precise quantitative assessment of intracranial atherosclerosis and plaque burden.