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dc.contributor.authorShen, Wei
dc.contributor.authorWang, Bin
dc.contributor.authorJiang, Yuan
dc.contributor.authorWang, Yan
dc.contributor.authorYuille, Alan L.
dc.date.accessioned2018-05-16T18:49:42Z
dc.date.available2018-05-16T18:49:42Z
dc.date.issued2017-10-01
dc.identifier.urihttp://hdl.handle.net/1721.1/115411
dc.description.abstractIn the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging problem, as it requires the detector to be able to detect both filament-like thin and blob-like thick membrane, while suppressing the ambiguous intracellular structure. In this paper, we propose multi-stage multi-recursive-input fully convolutional networks to address this problem. The multiple recursive inputs for one stage, i.e., the multiple side outputs with different receptive field sizes learned from the lower stage, provide multi-scale contextual boundary information for the consecutive learning. This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue. Our multi-stage networks are trained end-to-end. It achieves promising results on two public available EM segmentation datasets, the mouse piriform cortex dataset and the ISBI 2012 EM dataset.en_US
dc.description.sponsorshipThis material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216.en_US
dc.language.isoen_USen_US
dc.publisherCenter for Brains, Minds and Machines (CBMM)en_US
dc.relation.ispartofseriesCBMM Memo Series;080
dc.titleMulti-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detectionen_US
dc.typeTechnical Reporten_US
dc.typeWorking Paperen_US
dc.typeOtheren_US


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