  Semantic and instance segmentation algorithms are two general yet distinct
image segmentation solutions powered by Convolution Neural Network. While
semantic segmentation benefits extensively from the end-to-end training
strategy, instance segmentation is frequently framed as a multi-stage task,
supported by learning-based discrimination and post-process clustering.
Independent optimizations on substages instigate the accumulation of
segmentation errors. In this work, we propose to embed prior clustering
information into an embedding learning framework FCRNet, stimulating the
one-stage instance segmentation. FCRNet relieves the complexity of post process
by incorporating the number of clustering groups into the embedding space. The
superior performance of FCRNet is verified and compared with other methods on
the nucleus dataset BBBC006.
