Neuromuscular fiber segmentation using particle filtering and discrete optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00200726" target="_blank" >RIV/68407700:21230/12:00200726 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Neuromuscular fiber segmentation using particle filtering and discrete optimization

Popis výsledku v původním jazyce

'We present an algorithm to segment a set of parallel, intertwined and bifurcating fibers from 3D images, targeted for identification of neuronal fibers in very large sets of 3D confocal microscopy images. The method consists of preprocessing, local calculation of fiber probabilities, seed detection, local tracking by particle filtering, global supervised seed clustering, and final voxel segmentation. The preprocessing uses a novel random local probability filtering segmentation. The global segmentationis solved by discrete optimization. The combination of global and local approaches makes the segmentation robust, yet the individual data blocks can be processed sequentially, limiting memory consumption. The method is automatic but efficient manual interaction is possible if needed. Initial promising results on a neuromuscular projection fiber dataset as well as on simulated data are presented.

Název v anglickém jazyce

Neuromuscular fiber segmentation using particle filtering and discrete optimization

Popis výsledku anglicky

'We present an algorithm to segment a set of parallel, intertwined and bifurcating fibers from 3D images, targeted for identification of neuronal fibers in very large sets of 3D confocal microscopy images. The method consists of preprocessing, local calculation of fiber probabilities, seed detection, local tracking by particle filtering, global supervised seed clustering, and final voxel segmentation. The preprocessing uses a novel random local probability filtering segmentation. The global segmentationis solved by discrete optimization. The combination of global and local approaches makes the segmentation robust, yet the individual data blocks can be processed sequentially, limiting memory consumption. The method is automatic but efficient manual interaction is possible if needed. Initial promising results on a neuromuscular projection fiber dataset as well as on simulated data are presented.

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GAP202%2F11%2F0111" target="_blank" >GAP202/11/0111: Automatická analýza obrazů nervové tkáně ze světelné a elektronové mikroskopie</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů