Cell nucleus segmentation plays a significant role in Computer-Aided systems for cancer diagnosis. However, the nuclear images are characterized by different sizes, overlap, adhesion, and similarities between nuclei and other structures, making this task challenging. Aiming to adjust and enhance the feature learning ability of the network, this paper proposes a FourierFilter Irregular Attention U-Net (FFIA-UNet), which contains FourierFilter Irregular Attention (FFIA) and multi-receptive filed fusion (MRF) module. FFIA module seeks to learn deeper characteristics by taking advantage of frequency information and deformable convolution. MRF module improve the learning capacity of fuzzy edges and irregular forms via multiple dilated convolution. Experiments on three datasets show that the proposed FFIA-UNet achieves state-of-the-art. Dice-Score and mIoU reached 0.929 and 0.885 respectively on DSB2018. Furthermore, numerous ablation experiments have demonstrated the module’s efficacy.
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