注意力驱动的细胞团簇细胞核分割算法

Attention driven nuclei segmentation method for cell clusters

胸腔积液细胞团簇的细胞核形态为肺癌诊断、肿瘤转移及治疗效果评价提供了重要途径,而对其细胞核进行精准分割是肺癌病理诊断工作的基础。由于胸腔积液肿瘤细胞团簇复杂的生成背景,以及细胞核特征的不均匀性(特征信息分散)和团簇内部重叠细胞中的触核情况(特征不明显),使细胞团簇分割仍然是一个具有挑战性的问题。提出了基于注意力机制的改进U-Net模型,从空间注意力和通道注意力两方面来增强对细胞核非显著特征的学习;并改进U-Net的跳跃连接,融合U-Net中深层和浅层特征,解决语义间隙的问题。实验结果表明,与最新的其他方法相比,CRUNet能够在所建立的胸水细胞团簇数据集上取得更好的分割效果。为了进一步说明该网络的有效性,在公共数据集BBBC020上也与其他网络进行了对比。

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.