Learning context cues for synapse segmentation in EM volumes

Authors:
Carlos Becker;Karim Ali;Graham Knott;Pascal Fua
Affiliations:
Computer Vision Lab, École Polytechnique Fédérale de Lausanne, Switzerland;Computer Vision Lab, École Polytechnique Fédérale de Lausanne, Switzerland;Computer Vision Lab, École Polytechnique Fédérale de Lausanne, Switzerland;Computer Vision Lab, École Polytechnique Fédérale de Lausanne, Switzerland
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2012

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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.