摘要
细胞及细胞核的准确分割是宫颈癌计算机辅助筛查中的关键技术,针对具有重叠现象的宫颈细胞分割及其细胞核的提取,提出了一种U-Net网络语义分割下的多主动轮廓细胞分割提取方法。首先,对采集到的样本图像进行标注,将其分为背景、细胞、细胞核三部分;然后,对U-Net网络进行训练,并利用训练得到的模型对图像进行分语义分割,得到其中的细胞及细胞核区域;接着在UNet语义分割结果的基础上获得细胞团块信息,并通过像素点与细胞核之间的距离为每个细胞初始化一个水平集函数表示的细胞轮廓;最后,结合细胞的形状先验信息、图像的边缘信息和不同轮廓之间的相互信息建立水平集函数的能量泛函,通过最小化能量泛函得到细胞轮廓,最终完成每个细胞的分割。实验表明:文中提出的分割方法可以对复杂情况下的宫颈细胞进行分割,包括独立细胞和互相重叠的细胞及其细胞核,取得了良好的分割效果。
Accurate segmentation of cells and nuclei is the key technology in computer-assisted diagnosis of cervical cancer.In this paper,a multi-active contour method,which was based on semantic segmentation of the U-Net model,was proposed for overlapping cells segmentation.First,each sample image was labeled as three parts:background,cell and cell nucleus.Then,the U-Net model was trained to segment the cervical images to obtain the cell and cell nucleus;meanwhile,the cell clump information could be obtained.The active contour for each cell was initialized according to the distance from the pixel point to the cell nucleus,and the energy functional was established based on shape prior of cells,the edge prior of images and the mutual prior between different contours.Finally,every single cell was segmented by minimizing the proposed energy functional.Experiment comparisons show that the segmentation method proposed in this paper can segment cervical cells under complex conditions,including independent cells,overlapping cells and their nuclei.The experimental results also prove the effectiveness of proposed method.
作者
朱琳琳
韩璐
杜泓
范慧杰
Zhu Linlin;Han Lu;Du Hong;Fan Huijie(College of Automation,Shenyang Aerospace University,Shenyang 110135,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China)
出处
《红外与激光工程》
EI
CSCD
北大核心
2020年第S01期151-159,共9页
Infrared and Laser Engineering
关键词
宫颈细胞
主动轮廓
水平集
语义分割
细胞重叠
cervical cells
active contour
level set
semantic segmentation
cell overlap
