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

Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Assessment of a Novel Trainable Algorithm for Automated Segmentation of Multiple Islet Images.
Original language description
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
Czech name
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Czech description
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Project
<a href="/en/project/GAP202%2F11%2F0111" target="_blank" >GAP202/11/0111: Automatic analysis of light and electron microscopy neuronal data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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