基于注意力机制和残差结构的细胞核分割方法

病理细胞核的精准分割是病理学诊断的基础，然而当前算法针对带有核分裂象的乳腺癌细胞核自动分割效果差强人意。本文针对当前细胞核分割算法展开分析研究，并提出了一种基于注意力机制和残差结构相结合的U型网络(U＿net)用于解决因核分裂象和非核分裂象细胞形态特征十分接近造成细胞核分割精度不足的问题。通过实验表明本文提出算法的平均像素准确率(mean pixel accuracy,MPA)和Mean＿dice指标系数分别为0.74和0.82。与原有算法相比，训练指标分别提升了11%和9%,证明本文算法的可行性。

A cell nucleus segmentation method based on attention mechanism and residual structure

Accurate segmentation of pathological cell nucleus is the basis of pathological diagnosis,however,the current algorithm for automatic segmentation of mitotic breast cancer cell nucleus is poor.This paper analyze and study the current cell nucleus segmentation algorithm.And a U-shaped network (U_net) based on a combination of attention mechanism and residual structure is proposed to solve the problem of insufficient accuracy of cell nucleus segmentation due to the close morphological characteristics of mitotic and non-mitotic cells.The experiments show that the average pixel accuracy (MPA) and Mean_dice index coefficient of the proposed algorithm are 0.74 and 0.82. Compared with the original algorithm, the training indexes are improved by 11% and 9%,which proves the feasibility of the algorithm in this article.