  Most existing methods handle cell instance segmentation problems directly
without relying on additional detection boxes. These methods generally fails to
separate touching cells due to the lack of global understanding of the objects.
In contrast, box-based instance segmentation solves this problem by combining
object detection with segmentation. However, existing methods typically utilize
anchor box-based detectors, which would lead to inferior instance segmentation
performance due to the class imbalance issue. In this paper, we propose a new
box-based cell instance segmentation method. In particular, we first detect the
five pre-defined points of a cell via keypoints detection. Then we group these
points according to a keypoint graph and subsequently extract the bounding box
for each cell. Finally, cell segmentation is performed on feature maps within
the bounding boxes. We validate our method on two cell datasets with distinct
object shapes, and empirically demonstrate the superiority of our method
compared to other instance segmentation techniques. Code is available at:
https://github.com/yijingru/KG_Instance_Segmentation.

Computer Vision and Pattern Recognition