Abstract:

Nuclei morphology of pleural effusion cell clusters provides an essential way for the diagnosis,metastasis,and treatment evaluation of the lung cancer.Accurate segmentation of the nuclei is the basis of pathological diagnosis of the lung cancer.Because of the complex background of tumor cell clusters in pleural effusion,the inhomogeneity of nuclei features (scattered feature information),and nuclei overlapping within clusters (whose characteristics are not prominent),the segmentation of tumor cell clusters is still a challenging problem.In this paper,an improved U-Net model,named CRUNet,based on the attention mechanism is proposed.With the attention module,the CRUNet can enhance the learning of non-salient features of the nucleus from spatial attention and channel attention,and improve the jumping connection of the U-Net to integrate the deep and shallow features of the U-Net to solve the problem of the semantic gap.Experimental results show that compared with other state-of-the-art methods,the CRUNet can achieve a better segmentation performance on our self-established pleural effusion cell cluster dataset.To further illustrate the effectiveness of the proposed network,the CRUNet is also compared with other networks on a public cell dataset-BBBC020.

Key words: pleural effusion cell clusters, segmentation, attention mechanism, U-Net