  Automatic instance segmentation is a problem that occurs in many biomedical
applications. State-of-the-art approaches either perform semantic segmentation
or refine object bounding boxes obtained from detection methods. Both suffer
from crowded objects to varying degrees, merging adjacent objects or
suppressing a valid object. In this work, we assign an embedding vector to each
pixel through a deep neural network. The network is trained to output embedding
vectors of similar directions for pixels from the same object, while adjacent
objects are orthogonal in the embedding space, which effectively avoids the
fusion of objects in a crowd. Our method yields state-of-the-art results even
with a light-weighted backbone network on a cell segmentation (BBBC006 +
DSB2018) and a leaf segmentation data set (CVPPP2017). The code and model
weights are public available.
