TBENet:A two-branch boundary enhancement Network for cerebrovascular segmentation | IEEE Conference Publication | IEEE Xplore

TBENet:A two-branch boundary enhancement Network for cerebrovascular segmentation


Abstract:

Cerebrovascular segmentation in digital subtraction angiography (DSA) images is the gold standard for clinical diagnosis. However, owing to the complexity of cerebrovascu...Show More

Abstract:

Cerebrovascular segmentation in digital subtraction angiography (DSA) images is the gold standard for clinical diagnosis. However, owing to the complexity of cerebrovascular, automatic cerebrovascular segmentation in DSA images is a challenging task. In this paper, we propose a CNN-based Two-branch Boundary Enhancement Network (TBENet) for automatic segmentation of cerebrovascular in DSA images. The TBENet is inspired by U-Net and designed as an encoder-decoder architecture. We propose an additional boundary branch to segment the boundary of cerebrovascular and a Main and Boundary branches Fusion Module (MBFM) to integrate the boundary branch outcome with the main branch outcome to achieve better segmentation performance. The TBENet was evaluated on HMCDSA (an in-house DSA cerebrovascular dataset), and reaches 0.9611, 0.7486, 0.7152, 0.9860 and 0.9556 in Accuracy, F1 score, Sensitivity, Specificity, and AUC, respectively. Meanwhile, we tested our TBENet on the public vessel segmentation benchmark DRIVE, and the results show that our TBENet can be extended to diverse vessel segmentation tasks.
Date of Conference: 24-27 July 2023
Date Added to IEEE Xplore: 11 December 2023
ISBN Information:

ISSN Information:

PubMed ID: 38083508
Conference Location: Sydney, Australia

References

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