000178175 001__ 178175
000178175 005__ 20190316235415.0
000178175 037__ $$aCONF
000178175 245__ $$aLearning Context Cues for Synapse Segmentation in EM Volumes
000178175 269__ $$a2012
000178175 260__ $$c2012
000178175 336__ $$aConference Papers
000178175 490__ $$aLecture Notes in Computer Science
000178175 520__ $$aWe present a new approach for the automated segmentation of excitatory synapses in image stacks acquired by electron microscopy. We rely on a large set of image features specifically designed to take spatial context into account and train a classifier that can effectively utilize cues such as the presence of a nearby post-synaptic region. As a result, our algorithm successfully distinguishes synapses from the numerous other organelles that appear within an EM volume, including those whose local textural properties are relatively similar. This enables us to achieve very high detection rates with very few false positives.
000178175 6531_ $$aSynapse
000178175 6531_ $$aElectron Microscopy
000178175 6531_ $$aMedical Imaging
000178175 700__ $$0245786$$g211381$$aBecker, Carlos Joaquin
000178175 700__ $$0242723$$g179297$$aAli, Karim
000178175 700__ $$g159872$$aKnott, Graham$$0240043
000178175 700__ $$aFua, Pascal$$g112366$$0240252
000178175 7112_ $$aInternational Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
000178175 8564_ $$uhttps://infoscience.epfl.ch/record/178175/files/MICCA2012-synapse.pdf$$zn/a$$s3494666$$yn/a
000178175 909C0 $$xU10659$$0252087$$pCVLAB
000178175 909CO $$ooai:infoscience.tind.io:178175$$qGLOBAL_SET$$pconf$$pIC
000178175 917Z8 $$x211381
000178175 917Z8 $$x112366
000178175 937__ $$aEPFL-CONF-178175
000178175 973__ $$rREVIEWED$$sACCEPTED$$aEPFL
000178175 980__ $$aCONF