Accurate automated extraction of brain vessel centerlines from CTA images plays an important role in diagnosis and therapy of cerebrovascular diseases, such as stroke. However, this task remains challenging due to the complex cerebrovascular structure, the varying imaging quality, and vessel pathology effects. In this paper, we consider automatic lumen segmentation generation without additional annotation effort by physicians and more effective use of the generated lumen segmentation for improved centerline extraction performance. We propose an automated framework for brain vessel centerline extraction from CTA images. The framework consists of four major components: (1) pre-processing approaches that register CTA images with a CT atlas and divide these images into input patches, (2) lumen segmentation generation from annotated vessel centerlines using graph cuts and robust kernel regression, (3) a dual-branch topology-aware UNet (DTUNet) that can effectively utilize the annotated vessel centerlines and the generated lumen segmentation through a topology-aware loss (TAL) and its dual-branch design, and (4) post-processing approaches that skeletonize the predicted lumen segmentation. Extensive experiments on a multi-center dataset demonstrate that the proposed framework outperforms state-of-the-art methods in terms of average symmetric centerline distance (ASCD) and overlap (OV). Subgroup analyses further suggest that the proposed framework holds promise in clinical applications for stroke treatment. Code is publicly available at https://github.com/Liusj-gh/DTUNet.
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