755 research outputs found
Stateless actor-critic for instance segmentation with high-level priors
Instance segmentation is an important computer vision problem which remains
challenging despite impressive recent advances due to deep learning-based
methods. Given sufficient training data, fully supervised methods can yield
excellent performance, but annotation of ground-truth data remains a major
bottleneck, especially for biomedical applications where it has to be performed
by domain experts. The amount of labels required can be drastically reduced by
using rules derived from prior knowledge to guide the segmentation. However,
these rules are in general not differentiable and thus cannot be used with
existing methods. Here, we relax this requirement by using stateless actor
critic reinforcement learning, which enables non-differentiable rewards. We
formulate the instance segmentation problem as graph partitioning and the actor
critic predicts the edge weights driven by the rewards, which are based on the
conformity of segmented instances to high-level priors on object shape,
position or size. The experiments on toy and real datasets demonstrate that we
can achieve excellent performance without any direct supervision based only on
a rich set of priors
A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation
We propose a novel theoretical framework that generalizes algorithms for
hierarchical agglomerative clustering to weighted graphs with both attractive
and repulsive interactions between the nodes. This framework defines GASP, a
Generalized Algorithm for Signed graph Partitioning, and allows us to explore
many combinations of different linkage criteria and cannot-link constraints. We
prove the equivalence of existing clustering methods to some of those
combinations, and introduce new algorithms for combinations which have not been
studied. An extensive comparison is performed to evaluate properties of the
clustering algorithms in the context of instance segmentation in images,
including robustness to noise and efficiency. We show how one of the new
algorithms proposed in our framework outperforms all previously known
agglomerative methods for signed graphs, both on the competitive CREMI 2016 EM
segmentation benchmark and on the CityScapes dataset.Comment: 19 pages, 8 figures, 6 table
Microglia complement signaling promotes neuronal elimination and normal brain functional connectivity
Complement signaling is thought to serve as an opsonization signal to promote the phagocytosis of synapses by microglia. However, while its role in synaptic remodeling has been demonstrated in the retino-thalamic system, it remains unclear whether complement signaling mediates synaptic pruning in the brain more generally. Here we found that mice lacking the Complement receptor 3, the major microglia complement receptor, failed to show a deficit in either synaptic pruning or axon elimination in the developing mouse cortex. Instead, mice lacking Complement receptor 3 exhibited a deficit in the perinatal elimination of neurons in the cortex, a deficit that is associated with increased cortical thickness and enhanced functional connectivity in these regions in adulthood. These data demonstrate a role for complement in promoting neuronal elimination in the developing cortex
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