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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F12%3APU100206%21RIV13-MPO-26220___
rdf:type
n16:Vysledek skos:Concept
dcterms:description
This paper deals with the segmentation of neuronal struc- tures in electron microscope (EM) stacks, which is one of the challenges of the ISBI 2012 conference. The data for the challenge consists of a stack of 30 EM slices for training and 30 EM stacks for testing. The training data was labelled by an expert human neuroanatomist. In this paper a segmentation using local-level and segment-level features and machine learning algorithms was used. The results achieved on the ISBI 2012 challenge test set were: the Rand error: 0.139038440, warping er- ror: 0.002641296 and pixel error: 0.102285508. The main criterion for segmentation evaluation was the Rand error. This paper deals with the segmentation of neuronal struc- tures in electron microscope (EM) stacks, which is one of the challenges of the ISBI 2012 conference. The data for the challenge consists of a stack of 30 EM slices for training and 30 EM stacks for testing. The training data was labelled by an expert human neuroanatomist. In this paper a segmentation using local-level and segment-level features and machine learning algorithms was used. The results achieved on the ISBI 2012 challenge test set were: the Rand error: 0.139038440, warping er- ror: 0.002641296 and pixel error: 0.102285508. The main criterion for segmentation evaluation was the Rand error.
dcterms:title
Trainable Segmentation Based on Local-level and Segment-level Feature Extraction Trainable Segmentation Based on Local-level and Segment-level Feature Extraction
skos:prefLabel
Trainable Segmentation Based on Local-level and Segment-level Feature Extraction Trainable Segmentation Based on Local-level and Segment-level Feature Extraction
skos:notation
RIV/00216305:26220/12:PU100206!RIV13-MPO-26220___
n16:predkladatel
n17:orjk%3A26220
n3:aktivita
n14:S n14:P
n3:aktivity
P(FR-TI4/151), P(ME10123), S
n3:dodaniDat
n4:2013
n3:domaciTvurceVysledku
n12:8261571 n12:2629291 n12:9747397
n3:druhVysledku
n15:D
n3:duvernostUdaju
n8:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
174820
n3:idVysledku
RIV/00216305:26220/12:PU100206
n3:jazykVysledku
n18:eng
n3:klicovaSlova
segmentation, data mining, image processing
n3:klicoveSlovo
n13:data%20mining n13:image%20processing n13:segmentation
n3:kontrolniKodProRIV
[CD9AFB407E94]
n3:mistoKonaniAkce
Barcelona
n3:mistoVydani
Barcelona
n3:nazevZdroje
IEEE International Symposium on Biomedical Imaging
n3:obor
n20:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n5:FR-TI4%2F151 n5:ME10123
n3:rokUplatneniVysledku
n4:2012
n3:tvurceVysledku
Uher, Václav Mašek, Jan Burget, Radim
n3:typAkce
n9:WRD
n3:wos
000308143100071
n3:zahajeniAkce
2012-05-02+02:00
s:numberOfPages
63
n21:hasPublisher
Neuveden
n22:isbn
978-1-4673-1118-2
n7:organizacniJednotka
26220