title: Learning context cues for synapse segmentation in EM volumes.
abstract: We present a new approach for the automated segmentation of excitatory synapses in image stacks acquired by electron microscopy. We rely on a large set of image features specifically designed to take spatial context into account and train a classifier that can effectively utilize cues such as the presence of a nearby post-synaptic region. As a result, our algorithm successfully distinguishes synapses from the numerous other organelles that appear within an EM volume, including those whose local textural properties are relatively similar. This enables us to achieve very high detection rates with very few false positives.
authors.affiliation: Computer Vision Lab, Ecole Polytechnique Fédérale de Lausanne, Switzerland.
authors.forename: Carlos
authors.initials: C
authors.lastname: Becker
authors.forename: Karim
authors.initials: K
authors.lastname: Ali
authors.forename: Graham
authors.initials: G
authors.lastname: Knott
authors.forename: Pascal
authors.initials: P
authors.lastname: Fua
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000473:pathology*; D003463:Cues; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D051381:Rats; D015203:Reproducibility of Results; D012984:Software; D013003:Somatosensory Cortex / Q000473:pathology*; D013569:Synapses / Q000473:pathology*