  Instance segmentation of biological images is essential for studying object
behaviors and properties. The challenges, such as clustering, occlusion, and
adhesion problems of the objects, make instance segmentation a non-trivial
task. Current box-free instance segmentation methods typically rely on local
pixel-level information. Due to a lack of global object view, these methods are
prone to over- or under-segmentation. On the contrary, the box-based instance
segmentation methods incorporate object detection into the segmentation,
performing better in identifying the individual instances. In this paper, we
propose a new box-based instance segmentation method. Mainly, we locate the
object bounding boxes from their center points. The object features are
subsequently reused in the segmentation branch as a guide to separate the
clustered instances within an RoI patch. Along with the instance normalization,
the model is able to recover the target object distribution and suppress the
distribution of neighboring attached objects. Consequently, the proposed model
performs excellently in segmenting the clustered objects while retaining the
target object details. The proposed method achieves state-of-the-art
performances on three biological datasets: cell nuclei, plant phenotyping
dataset, and neural cells.
