Abstract: A white matter hyperintensity segmentation method called PGF-Net is proposed,which combines local and global perception with semantic flow alignment,to address the characteristic of small targets in high signal white matter.Firstly,it proposes the PAA(Patch Aware Attention) module,which enhances the ability to extract local features by dividing local small image blocks for feature selection.Secondly,it proposes to combine local and global aware attention modules(PGAA) and utilizes the characteristics of Transformer global perception to establish long-range dependencies.Lastly,it proposes a gated flow alignment module(GFAM) to predict the semantic flow offset field in the decoding section.Guide the expansion of high-level features in the decoder to achieve precise alignment and fusion with the corresponding low-level features in the encoder.Experimental results show that the PGF-Net achieves optimal performance in a self collected dataset,with a cross union ratio(mIoU) of 0.876 9,a Dice coefficient of 0.842 3,a Hausdorff distance(HD) of 32.61,and an average surface distance(ASD) of only 1.7.The model also achieves optimal performance on two small target public datasets,verifying its generalization and robustness.This method has certain application prospects in assisting doctors in diagnosis in the future.

Key words: Image segmentation, Small target, Local perception, Global perception, Semantic flow alignment