Assessment of a Novel Trainable Algorithm for Automated Segmentation of Multiple Islet Images.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00217904" target="_blank" >RIV/68407700:21230/14:00217904 - 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

Assessment of a Novel Trainable Algorithm for Automated Segmentation of Multiple Islet Images.

Popis výsledku v původním jazyce

Accurate sampling of islet graft suspension is confounded by the islet size heterogeneity. Assessment of multiple samples is advisable. Current islet counting methods remain time- and labour-intensive. We tested precision of automated assessment of multiple islet images by a simple learning algorithm. We generated the ground truth upon which a trainable algorithm was developed. The ground truth consisted of 12 islet images manually segmented in triplicates by four experienced operators using the gray-level thresholding. Next, training of Linear Perceptron algorithm (features = RGB) on individual images generated automatic classifiers, which in turn were used to assess islet images (dithizone-stained human islets with 40% exocrine tissue). The areas assigned to individual islets were converted to the islet equivalents (IE) using extended Ricordi table, d=2sqrt(area/ pi). The first twelve images were segmented in triplicates by four experienced operators using manual thresholding. The co

Název v anglickém jazyce

Assessment of a Novel Trainable Algorithm for Automated Segmentation of Multiple Islet Images.

Popis výsledku anglicky

Accurate sampling of islet graft suspension is confounded by the islet size heterogeneity. Assessment of multiple samples is advisable. Current islet counting methods remain time- and labour-intensive. We tested precision of automated assessment of multiple islet images by a simple learning algorithm. We generated the ground truth upon which a trainable algorithm was developed. The ground truth consisted of 12 islet images manually segmented in triplicates by four experienced operators using the gray-level thresholding. Next, training of Linear Perceptron algorithm (features = RGB) on individual images generated automatic classifiers, which in turn were used to assess islet images (dithizone-stained human islets with 40% exocrine tissue). The areas assigned to individual islets were converted to the islet equivalents (IE) using extended Ricordi table, d=2sqrt(area/ pi). The first twelve images were segmented in triplicates by four experienced operators using manual thresholding. The co

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

2014

Kód důvěrnosti údajů

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