Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neuroscience

Background:

• Cerebrovascular segmentation from TOF-MRA and CTA is crucial for diagnosing and planning treatments for intracranial vascular diseases.
• Deep learning models face challenges due to expensive annotations and performance degradation across different imaging modalities.
• Distinct imaging principles in TOF-MRA and CTA create domain discrepancies, hindering model generalization.

Purpose of the Study:

• To develop an unsupervised domain adaptation framework for cross-modality unpaired cerebral angiography translation and segmentation.
• To address the limitations of expensive annotations and performance degradation in deep learning models for cerebrovascular segmentation.
• To improve the accuracy and robustness of deep learning models for analyzing intracranial vasculature across different imaging techniques.

Main Methods:

• Proposed CereTS, an unsupervised domain adaptation framework utilizing multi-level domain alignment.
• Incorporated image-level cyclic geometric consistency, patch-level masked contrastive constraint, and feature-level semantic perception.
• Focused on shrinking domain discrepancy while preserving vascular structure consistency.

Main Results:

• CereTS demonstrated superior performance in cross-modality translation and segmentation of cerebral angiography.
• The framework effectively reduced domain discrepancy between TOF-MRA and CTA datasets.
• Achieved significant performance improvements over current state-of-the-art methods on public and private datasets.

Conclusions:

• CereTS offers a robust solution for unsupervised domain adaptation in cerebrovascular segmentation.
• The proposed multi-level domain alignment effectively handles cross-modality variations in angiography.
• This framework has the potential to enhance diagnostic and treatment planning for cerebrovascular diseases.