  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.
