  Cell instance segmentation is a new and challenging task aiming at joint
detection and segmentation of every cell in an image. Recently, many instance
segmentation methods have applied in this task. Despite their great success,
there still exists two main weaknesses caused by uncertainty of localizing cell
center points. First, densely packed cells can easily be recognized into one
cell. Second, elongated cell can easily be recognized into two cells. To
overcome these two weaknesses, we propose a novel cell instance segmentation
network based on multi-scheme regression guidance. With multi-scheme regression
guidance, the network has the ability to look each cell in different views.
Specifically, we first propose a gaussian guidance attention mechanism to use
gaussian labels for guiding the network's attention. We then propose a
point-regression module for assisting the regression of cell center. Finally,
we utilize the output of the above two modules to further guide the instance
segmentation. With multi-scheme regression guidance, we can take full advantage
of the characteristics of different regions, especially the central region of
the cell. We conduct extensive experiments on benchmark datasets, DSB2018,
CA2.5 and SCIS. The encouraging results show that our network achieves SOTA
(state-of-the-art) performance. On the DSB2018 and CA2.5, our network surpasses
previous methods by 1.2% (AP50). Particularly on SCIS dataset, our network
performs stronger by large margin (3.0% higher AP50). Visualization and
analysis further prove that our proposed method is interpretable.
