  Instance segmentation is an important task for biomedical and biological
image analysis. Due to the complicated background components, the high
variability of object appearances, numerous overlapping objects, and ambiguous
object boundaries, this task still remains challenging. Recently, deep learning
based methods have been widely employed to solve these problems and can be
categorized into proposal-free and proposal-based methods. However, both
proposal-free and proposal-based methods suffer from information loss, as they
focus on either global-level semantic or local-level instance features. To
tackle this issue, we present a Panoptic Feature Fusion Net (PFFNet) that
unifies the semantic and instance features in this work. Specifically, our
proposed PFFNet contains a residual attention feature fusion mechanism to
incorporate the instance prediction with the semantic features, in order to
facilitate the semantic contextual information learning in the instance branch.
Then, a mask quality sub-branch is designed to align the confidence score of
each object with the quality of the mask prediction. Furthermore, a consistency
regularization mechanism is designed between the semantic segmentation tasks in
the semantic and instance branches, for the robust learning of both tasks.
Extensive experiments demonstrate the effectiveness of our proposed PFFNet,
which outperforms several state-of-the-art methods on various biomedical and
biological datasets.
