Machines that learn to segment images: a crucial technology for connectomics.
about
Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy imagesAutomated image computing reshapes computational neuroscienceBiomedical Visual Computing: Case Studies and ChallengesLarge-scale automated histology in the pursuit of connectomesThe big data problem: turning maps into knowledgeDeep molecular diversity of mammalian synapses: why it matters and how to measure itComputational methods and challenges for large-scale circuit mappingNeuronal cell types and connectivity: lessons from the retinaTrakEM2 software for neural circuit reconstructionDecreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed NetworksMapping Synaptic Input Fields of Neurons with Super-Resolution Imaging.Crowdsourcing the creation of image segmentation algorithms for connectomics.Progress Towards Mammalian Whole-Brain Cellular ConnectomicsThe Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput NeuroscienceUnderstanding complexity in the human brain.Multiscale exploration of mouse brain microstructures using the knife-edge scanning microscope brain atlas.Input clustering and the microscale structure of local circuits.Beyond counts and shapes: studying pathology of dendritic spines in the context of the surrounding neuropil through serial section electron microscopy.Large-volume reconstruction of brain tissue from high-resolution serial section images acquired by SEM-based scanning transmission electron microscopyComputer assisted assembly of connectomes from electron micrographs: application to Caenorhabditis elegansThe non-random brain: efficiency, economy, and complex dynamicsFindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysisSpecificity and randomness: structure-function relationships in neural circuitsFIB/SEM technology and high-throughput 3D reconstruction of dendritic spines and synapses in GFP-labeled adult-generated neurons.Unsupervised segmentation of low-contrast multichannel images: discrimination of tissue components in microscopic images of unstained specimensDirect maximization of protein identifications from tandem mass spectraA platform for brain-wide imaging and reconstruction of individual neurons.3-dimensional electron microscopic imaging of the zebrafish olfactory bulb and dense reconstruction of neurons.Vectorization of optically sectioned brain microvasculature: learning aids completion of vascular graphs by connecting gaps and deleting open-ended segmentsComparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients.OXPHOS mutations and neurodegenerationExtracellular space preservation aids the connectomic analysis of neural circuitsQuantitative neuroanatomy for connectomics in DrosophilaA high-throughput framework to detect synapses in electron microscopy images.Semi-automated neuron boundary detection and nonbranching process segmentation in electron microscopy images.Molecular neuroanatomy: a generation of progress.The big and the small: challenges of imaging the brain's circuits.From functional architecture to functional connectomics.High resolution imaging of neuronal connectivity.Cellular-resolution connectomics: challenges of dense neural circuit reconstruction.
P2860
Q21090988-8EAD0046-3A1B-4B95-B986-6A1ECA65EC80Q21284306-00144937-C084-4DB5-862E-B9DDF5F7A18EQ24633380-BF065FE7-54B8-4C14-BBDF-BB422973F779Q26828928-CB702F69-DEA3-4626-9674-FDBB47805AB2Q26992172-A3E29645-FB1A-4902-9CBF-DC045A2AC21DQ26998700-A6552480-DD52-4E43-AE5B-38F11C08E413Q27021213-E597818F-EF1D-48B2-B798-0983A8BB7B53Q27021497-09F9086B-5A6E-4A11-BD0B-93968B7F7FAFQ27301965-11DD0A64-B7EC-4A95-BEB3-057A5FF92EB5Q27318476-79E09024-835E-44FC-BE1A-0FF144689FE7Q27322226-B09456B9-6D42-4F48-B65C-673D36A033CDQ27322860-BC322C31-A6AD-4DB0-A361-FC6CA8424581Q28112154-858BFBD9-62F0-4A6A-A7EE-E802B9D41657Q28660841-47A78830-B6BB-48AC-A052-90D012F939FDQ30401906-AC7C3C03-30E6-4FCA-A3AB-DD45F90CB07DQ34140028-B7023140-BE8E-44EC-A09B-569369352AA8Q34171287-4D4F188C-CE78-4F80-9881-69715FA4C6F2Q34273065-00E09B94-25B5-4341-BF9F-28F8A7BD9F54Q34453766-04B2210B-CE13-405B-AA44-C60D4681BB55Q34559077-6B539730-E99F-4585-8201-716C4AA90B66Q34569006-D1181DF2-E9C5-410D-A72E-842895A8F107Q34640823-3E20C204-B207-4A00-B2E3-8D7409D394D7Q35570945-CC890CB3-CE0A-4455-9E87-853744C6FFCBQ35633118-27FA22F4-873A-4286-84C9-33EB3AD1F742Q35671199-8E92325D-02B0-45F3-A571-3611BD7FF7C3Q35751671-2040CF20-57C7-408D-9ACD-167F5FB08411Q35900458-BFFE4179-9CAE-43DE-B08E-E6C4503B9110Q36185456-BFD33C1C-7893-4243-A818-084EB1577E4CQ36236709-FB58090B-A3AD-4A18-A30B-D88E17DAB45BQ36445803-6531A0E7-6B88-4975-878C-038BED628B93Q36533623-DA6D8993-9228-4303-8033-51535650A348Q36610062-8E359F43-4F45-4299-BCDD-4F11D237BF06Q36739757-E42436A1-3CF0-4046-9B73-1E00F030F186Q36960689-FE45EB4F-35B3-417F-A523-D401784B0703Q37557585-B18124C8-3186-415F-99BD-15166B30F8B0Q37624780-681F5873-D216-4155-ADDE-354D5D185589Q37952681-4375ED49-2758-4E93-AB75-440676BEC88FQ38030190-D9A4AF31-D3C2-4FD7-AAF5-6293DD83AC87Q38033601-F53DF549-BC06-40CD-83BA-E692A8363AF2Q38110686-7034A954-CB1B-45AE-92CB-5CD03A9526BF
P2860
Machines that learn to segment images: a crucial technology for connectomics.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on October 2010
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Machines that learn to segment images: a crucial technology for connectomics.
@en
Machines that learn to segment images: a crucial technology for connectomics.
@nl
type
label
Machines that learn to segment images: a crucial technology for connectomics.
@en
Machines that learn to segment images: a crucial technology for connectomics.
@nl
prefLabel
Machines that learn to segment images: a crucial technology for connectomics.
@en
Machines that learn to segment images: a crucial technology for connectomics.
@nl
P2093
P2860
P1476
Machines that learn to segment images: a crucial technology for connectomics.
@en
P2093
H Sebastian Seung
Srinivas C Turaga
Viren Jain
P2860
P304
P356
10.1016/J.CONB.2010.07.004
P577
2010-10-01T00:00:00Z