A Novel Mis-Seg-Focus Loss Function Based on a Two-Stage nnU-Net Framework for Accurate Brain Tissue Segmentation

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neuroscience

Background:

• Accurate brain tissue segmentation is crucial for diagnosing and treating neurological disorders.
• Distinguishing overlapping brain tissue boundaries poses a significant challenge for current segmentation methods.
• Existing deep learning models often propagate errors from local mis-segmentations, impacting overall accuracy.

Purpose of the Study:

• To develop a novel mis-segmentation-focused loss function for enhanced brain tissue segmentation.
• To improve the handling of ambiguous boundaries and overlapping anatomical structures in deep learning models.
• To achieve more precise and reliable brain tissue segmentation results.

Main Methods:

• Implementation of a two-stage nnU-Net framework.
• Utilizing a global loss function in the first stage to identify mis-segmented regions.
• Employing a specialized mis-segmentation loss function in the second stage for adaptive model adjustment.

Main Results:

• The proposed method demonstrated superior performance compared to existing approaches.
• Quantitative and qualitative evaluations confirmed the effectiveness of the novel loss function.
• Improved segmentation accuracy, particularly in challenging regions with overlapping tissues.

Conclusions:

• The novel mis-segmentation-focused loss function significantly enhances brain tissue segmentation accuracy.
• The two-stage approach effectively addresses limitations of current deep learning segmentation methods.
• This method offers a promising advancement for clinical applications in neuroimaging.