  Accurate segmenting nuclei instances is a crucial step in computer-aided
image analysis to extract rich features for cellular estimation and following
diagnosis as well as treatment. While it still remains challenging because the
wide existence of nuclei clusters, along with the large morphological variances
among different organs make nuclei instance segmentation susceptible to
over-/under-segmentation. Additionally, the inevitably subjective annotating
and mislabeling prevent the network learning from reliable samples and
eventually reduce the generalization capability for robustly segmenting unseen
organ nuclei. To address these issues, we propose a novel deep neural network,
namely Contour-aware Informative Aggregation Network (CIA-Net) with multi-level
information aggregation module between two task-specific decoders. Rather than
independent decoders, it leverages the merit of spatial and texture
dependencies between nuclei and contour by bi-directionally aggregating
task-specific features. Furthermore, we proposed a novel smooth truncated loss
that modulates losses to reduce the perturbation from outliers. Consequently,
the network can focus on learning from reliable and informative samples, which
inherently improves the generalization capability. Experiments on the 2018
MICCAI challenge of Multi-Organ-Nuclei-Segmentation validated the effectiveness
of our proposed method, surpassing all the other 35 competitive teams by a
significant margin.
