Keywords: AI/ML Image Reconstruction, Vessels
Motivation: Addressing the challenge of segmenting cerebral vessels in TOF-MRA images, we explored transfer learning to overcome the need for large, annotated datasets.
Goal(s): Assess the feasibility of using a refined CTA-based 3D CNN model for MRA vascular segmentation with a limited dataset.
Approach: Implemented transfer learning on a ResU-Net3 model, initially trained on CTA scans, fine-tuned with a small MRA dataset.
Results: Post-transfer learning, the model's DSC improved dramatically, indicating effective MRA vessel segmentation with limited data.
Impact: This study benefits radiologists by streamlining the segmentation of cerebral vessels in MRA, reducing the workload associated with annotation. The method has the potential to be integrated into clinical workflows, enhancing the efficacy of vascular reconstruction in clinical settings.
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