高技术通讯2026，Vol.36Issue(7)：701-712,12.DOI:10.3772/j.issn.1002-0470.2026.07.004
基于多尺度分形维数加权损失和注意力机制的鸡胚胎血管分割方法
Segmentation of chicken embryo vascular network based on multiscale fractal dimension weighted loss and attention mechanism
摘要
Abstract
In vivo microscopy images are characterized by high noise and strong dynamics,and traditional vascular seg-mentation methods often encounter problems such as vascular discontinuity and prediction errors in addressing these challenges.To address this issue,this study designs a deep learning model based on multi-scale fractal dimension-weighted loss and attention mechanism.This model integrates the spatial complexity of the vascular network into the training process through fractal dimension-weighted loss and shallow attention mechanisms,while preserving the ad-vantages of multi-layer feature extraction and global information through multi-scale structures and deep attention mechanisms.The model is trained and tested on a proprietary dataset of chicken embryo vascular network images,and its segmentation performance is evaluated through the number of vascular discontinuities and traditional met-rics.The results show that the proposed model increases the Dice(Dice coefficient)index from 0.896 to 0.906,the mean intersection over union(mIoU)from0.813 to0.830,the accuracy from0.885 to0.938,the area under curve(AUC)from 0.914 to0.930,and reduces the average number of vascular discontinuity points from15.7 to 11.3.This indicates that the model effectively reduces the number of vascular discontinuities while maintaining seg-mentation performance,thereby improving the connectivity of the vascular network structure.关键词
血管分割/注意力机制/分型维数/多尺度损失函数/活体显微镜成像Key words
vessel segmentation/attention mechanism/fractal dimension/multiscale loss function/in vivo microscopic imaging引用本文复制引用
潘清,王硕,吕雪倩..基于多尺度分形维数加权损失和注意力机制的鸡胚胎血管分割方法[J].高技术通讯,2026,36(7):701-712,12.基金项目
国家自然科学基金(31870938,32371372)资助项目. （31870938,32371372）
