基于迁移学习与Attention U-Net的同步辐射非人灵长类脑血管图像分割算法研究OA

[Objective]To address the challenges of scarce high-quality annotations and severe artifact interference in Syn-chrotron Radiation Micro-Tomography(SR-μCT)imaging of non-human primate cerebrovasculature,this study proposes an automated segmentation method based on transfer learning and a hierarchical combined weighting strategy.[Methods]A global percentile normalization strategy is employed to suppress artifacts while maintain-ing signal consistency.A"pre-training and fine-tuning"framework is established,innovatively incorporating a hi-erarchical combined weighting strategy to synergistically reinforce the capture of micro-vessels and boundary fea-tures.[Results]Experimental results demonstrate that the proposed method comprehensively outperforms nnU-Net 3D,achieving a Dice coefficient of 0.8686,a recall of 96.93%,and a topological connectivity metric(clDice)of 0.8848.These results signify a substantial resolution to the issues of micro-vessel miss-detection and discon-nection.[Conclusions]This algorithm effectively overcomes the segmentation challenges associated with small-sample and high-resolution SR-μCT cerebrovascular imaging,laying a solid technical and data foundation for fu-ture large-scale sub-micron whole-brain imaging analysis and neurovascular network quantification in non-hu-man primates and humans.