English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a method for segmenting neuron membranes in 2D electron microscopyimagery. This segmentation task has been a bottleneck to reconstruction effortsof the brain's synaptic circuits. One common problem is the misclassificationof blurry membrane fragments as cell interior, which leads to merging of twoadjacent neuron sections into one via the blurry membrane region. Humanannotators can easily avoid such errors by implicitly performing gapcompletion, taking into account the continuity of membranes.  Drawing inspiration from these human strategies, we formulate thesegmentation task as an edge labeling problem on a graph with local topologicalconstraints. We derive an integer linear program (ILP) that enforces membranecontinuity, i.e. the absence of gaps. The cost function of the ILP is thepixel-wise deviation of the segmentation from a priori membrane probabilitiesderived from the data.  Based on membrane probability maps obtained using random forest classifiersand convolutional neural networks, our method improves the neuron boundarysegmentation accuracy compared to a variety of standard segmentationapproaches. Our method successfully performs gap completion and leads to fewertopological errors. The method could potentially also be incorporated intoother image segmentation pipelines with known topological constraints.

Efficient 2D neuron boundary segmentation with local topological constraints