基于全局特征引导和注意力的宫颈细胞核分割

Cervical nuclear segmentation based on global feature guidanceand attention

针对宫颈细胞中正常和异常细胞核大小形态各异、细胞图像中存在干扰物影响分割等问题,本文提出一种基于全局特征引导和注意力的分割网络来提高细胞核分割精度。首先以U型网络结构作为主体,引入全局特征引导模块,充分提取各个阶段的特征获得不同层次的全局上下文信息,克服了U型网络单阶段上下文信息提取能力不足的问题以更好地处理不同形状的细胞核,提高边缘分割精度;其次,引入改进的注意门结构抑制图像中干扰物的信息,突出细胞核的信息,提高模型对干扰信息的辨别能力。在Herlev数据集上的实验结果表明,本文的方法能够有效地提高细胞核分割精度,在定量分析中Dice系数为0.9413,相较于其他方法具有一定的优势。

This paper presents a segmentation network that utilizes global feature guidance and attention to enhance the precision of cell segmentation,addressing the variability in size and shape of normal and abnormal nuclei in cervical cells as well as interference in cell images.Firstly,the U-shaped network structure is utilized as the main framework,with the introduction of a global feature guide module to comprehensively extract features at each stage for obtaining global context information at different levels.This overcomes the limitation of insufficient extraction ability of single-stage context information in U-shaped networks,enabling better handling of nuclei with different shapes and improving edge segmentation accuracy.Secondly,an improved attention-gate structure is incorporated to suppress interfering information in images,emphasize nucleus information,and enhance model discrimination against interfering data.Experimental results on the Herlev dataset demonstrate that our proposed method effectively enhances nuclear segmentation precision,achieving a Dice coefficient of 0.9413 in quantitative analysis which presents certain advantages compared to other methods.