Pap smear testing has been widely used for detecting cervical cancers based on the morphology properties of cell nuclei in microscopic image. An accurate nuclei segmentation could thus improve the success rate of cervical cancer screening. In this work, a method of automated cervical nuclei segmentation using Deformable Multipath Ensemble Model (D-MEM) is proposed. The approach adopts a U-shaped convolutional network as a backbone network, in which dense blocks are used to transfer feature information more effectively. To increase the flexibility of the model, we then use deformable convolution to deal with different nuclei irregular shapes and sizes. To reduce the predictive bias, we further construct multiple networks with different settings, which form an ensemble model. The proposed segmentation framework has achieved state-of-the-art accuracy on Herlev dataset with Zijdenbos similarity index (ZSI) of 0.933, and has the potential to be extended for solving other medical image segmentation tasks.

本文提出了一种使用可变形多路径集合模型（D-MEM）进行自动宫颈细胞核分割的方法，其中采用U形卷积网络作为主干网络，并使用多个具有不同设置的网络构建集合模型，以提高模型的灵活性和准确性，该分割框架在Herlev数据集上实现了最新的精度，并具有解决其他医学图像分割任务的潜力。

基于可变形多路径集合模型的宫颈涂片细胞核自动分割