  We share our recent findings in an attempt to train a universal segmentation
network for various cell types and imaging modalities. Our method was built on
the generalized U-Net architecture, which allows the evaluation of each
component individually. We modified the traditional binary training targets to
include three classes for direct instance segmentation. Detailed experiments
were performed regarding training schemes, training settings, network
backbones, and individual modules on the segmentation performance. Our proposed
training scheme draws minibatches in turn from each dataset, and the gradients
are accumulated before an optimization step. We found that the key to training
a universal network is all-time supervision on all datasets, and it is
necessary to sample each dataset in an unbiased way. Our experiments also
suggest that there might exist common features to define cell boundaries across
cell types and imaging modalities, which could allow application of trained
models to totally unseen datasets. A few training tricks can further boost the
segmentation performance, including uneven class weights in the cross-entropy
loss function, well-designed learning rate scheduler, larger image crops for
contextual information, and additional loss terms for unbalanced classes. We
also found that segmentation performance can benefit from group normalization
layer and Atrous Spatial Pyramid Pooling module, thanks to their more reliable
statistics estimation and improved semantic understanding, respectively. We
participated in the 6th Cell Tracking Challenge (CTC) held at IEEE
International Symposium on Biomedical Imaging (ISBI) 2021 using one of the
developed variants. Our method was evaluated as the best runner up during the
initial submission for the primary track, and also secured the 3rd place in an
additional round of competition in preparation for the summary publication.
