  Instance segmentation is of great importance for many biological
applications, such as study of neural cell interactions, plant phenotyping, and
quantitatively measuring how cells react to drug treatment. In this paper, we
propose a novel box-based instance segmentation method. Box-based instance
segmentation methods capture objects via bounding boxes and then perform
individual segmentation within each bounding box region. However, existing
methods can hardly differentiate the target from its neighboring objects within
the same bounding box region due to their similar textures and low-contrast
boundaries. To deal with this problem, in this paper, we propose an
object-guided instance segmentation method. Our method first detects the center
points of the objects, from which the bounding box parameters are then
predicted. To perform segmentation, an object-guided coarse-to-fine
segmentation branch is built along with the detection branch. The segmentation
branch reuses the object features as guidance to separate target object from
the neighboring ones within the same bounding box region. To further improve
the segmentation quality, we design an auxiliary feature refinement module that
densely samples and refines point-wise features in the boundary regions.
Experimental results on three biological image datasets demonstrate the
advantages of our method. The code will be available at
https://github.com/yijingru/ObjGuided-Instance-Segmentation.
