GEODESIC CURVATURE FLOW ON SURFACES FOR AUTOMATIC SULCAL DELINEATION

Area of Science:

• Neuroimaging
• Computational Anatomy
• Medical Image Analysis

Background:

• Sulcal folds (sulci) are crucial for understanding brain development and neurological conditions.
• Automated and precise sulcal curve delineation is challenging due to significant inter-individual shape variations.

Purpose of the Study:

• To develop an automatic and accurate method for delineating sulcal curves on brain surfaces.
• To refine the localization of sulcal curves using geodesic curvature flow.

Main Methods:

• A geodesic curvature flow method is proposed, utilizing an atlas brain surface mesh.
• Sulcal curves are transferred to subject brains via surface-based registration and refined using geodesic curvature flow.
• A level set formulation on non-flat surfaces and curvature-based weighting are employed, with discretization using finite elements on triangulated meshes.

Main Results:

• The method successfully refines the positions of sulcal curves to better match the true sulcal fundi.
• Validation demonstrates accurate delineation by comparing automated curves against manually delineated ones.

Conclusions:

• The proposed geodesic curvature flow method offers an accurate and automatic approach for sulcal curve delineation.
• This technique has potential applications in analyzing brain development and disease through improved neuroimaging analysis.