Dual-Branch Vascular Segmentation Model Based on Anisotropic Attention

Vascular segmentation is significant for diagnosing and treating vascular diseases. However, because of the fuzzy boundary of vessels, the variable shape of diseased vessels, and the significant differences between different samples, the segmentation model should accurately determine the differences between vessels and background classes and analyze the connectivity within vessels. This study proposes a novel three-dimensional vascular segmentation network, CAU-Net, based on centerline constraints and anisotropic attention. In response to the difficulties in vascular segmentation, the basic network structure, ResU-Net, is improved to construct an anisotropic attention module. This module extracts vascular spatial anisotropic features from three directions based on the unique spatial anisotropy of the vascular structure and models the correlation between feature channels to learn the three-dimensional spatial information of the vessels. By using the main auxiliary dual-branch model, b-Net performs semantic segmentation on vessels, whereas a-Net learns the continuity features of vessel centerlines, constrains the vascular segmentation results of b-Net, and ensures the integrity of the vascular segmentation results. The experimental results on the publicly available dataset 3D-IRCADb-01 shows that for the segmentation of portal and hepatic veins, CAU-Net achieves Dice coefficients of (74.80±8.05)% and (76.14±6.89)%, NSD coefficients of(54.80±8.09)% and(50.40±5.22)%, clDice coefficients of (72.43±8.26)% and(70.84±6.05)%, Branch Detection(BD) rates of (46.47±12.89)% and(39.19±7.97)%, and Tree length Detection(TD) rates of(67.08±15.59)% and(61.47±9.32)%, respectively. Component ablation experiments are conducted on the publicly available cerebrovascular dataset IXI, and the average Dice, NSD, clDice, BD, and TD values of the model on the validation set are(94.11±0.39)%, (96.53±0.37)%, (95.83±0.59)%, (98.64±1.63)%, and(95.44±1.22)%, respectively. Compared to the Baseline, the average Dice, NSD, clDice, BD, and TD values of the proposed model increased by 0.92%, 0.82%, 0.92%, 1.11%, and 1.60%, respectively. The CAU-Net vascular segmentation model can significantly improve the accuracy and completeness of vascular segmentation.

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