  Nuclei segmentation, despite its fundamental role in histopathological image
analysis, is still a challenge work. The main challenge of this task is the
existence of overlapping areas, which makes separating independent nuclei more
complicated. In this paper, we propose a new two-branch architecture by
combining the Unet and TransUnet networks for nuclei segmentation task. In the
proposed architecture, namely Trans2Unet, the input image is first sent into
the Unet branch whose the last convolution layer is removed. This branch makes
the network combine features from different spatial regions of the input image
and localizes more precisely the regions of interest. The input image is also
fed into the second branch. In the second branch, which is called TransUnet
branch, the input image will be divided into patches of images. With Vision
transformer (ViT) in architecture, TransUnet can serve as a powerful encoder
for medical image segmentation tasks and enhance image details by recovering
localized spatial information. To boost up Trans2Unet efficiency and
performance, we proposed to infuse TransUnet with a computational-efficient
variation called "Waterfall" Atrous Spatial Pooling with Skip Connection
(WASP-KC) module, which is inspired by the "Waterfall" Atrous Spatial Pooling
(WASP) module. Experiment results on the 2018 Data Science Bowl benchmark show
the effectiveness and performance of the proposed architecture while compared
with previous segmentation models.
