  Automatic cell segmentation is an essential step in the pipeline of
computer-aided diagnosis (CAD), such as the detection and grading of breast
cancer. Accurate segmentation of cells can not only assist the pathologists to
make a more precise diagnosis, but also save much time and labor. However, this
task suffers from stain variation, cell inhomogeneous intensities, background
clutters and cells from different tissues. To address these issues, we propose
an Attention Enforced Network (AENet), which is built on spatial attention
module and channel attention module, to integrate local features with global
dependencies and weight effective channels adaptively. Besides, we introduce a
feature fusion branch to bridge high-level and low-level features. Finally, the
marker controlled watershed algorithm is applied to post-process the predicted
segmentation maps for reducing the fragmented regions. In the test stage, we
present an individual color normalization method to deal with the stain
variation problem. We evaluate this model on the MoNuSeg dataset. The
quantitative comparisons against several prior methods demonstrate the
superiority of our approach.
