| Attributes | Values |
|---|
| rdf:type
| |
| 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 coefficient of variation (CV) was 0.07?0.03 (n=36). Next, the training of the automatic classifiers on the same set of images reached similar precision (CV 0.07?0.04, n=48). These 48 trained classifiers automatically segmented the remaining 11 images with variation similar to that reported for the manual islet counting (median CV 0.08, range 0.03-0.16, n=528). Additional 25 classifiers were trained on 25 images of a single swirled-rearranged tissue sample, and then used to assess the remaining 24 images. The islet designated area (CV 0.11-0.12, n=600) was translated into average of 150 IE per sample (range 140-167 IE, CV 0.13, n=600). Anaysis of 25 images by a trained classifier required 3 minutes including the training of the classifier. CONCLUSIONS: The data demonstrate feasibility of utilisation of a trainable algorithm for rapid analysis of multiple islet images. Development of more advanced algorithm is under way. Supported by Health Ministry, Czech Republic NT/13099
- 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 coefficient of variation (CV) was 0.07?0.03 (n=36). Next, the training of the automatic classifiers on the same set of images reached similar precision (CV 0.07?0.04, n=48). These 48 trained classifiers automatically segmented the remaining 11 images with variation similar to that reported for the manual islet counting (median CV 0.08, range 0.03-0.16, n=528). Additional 25 classifiers were trained on 25 images of a single swirled-rearranged tissue sample, and then used to assess the remaining 24 images. The islet designated area (CV 0.11-0.12, n=600) was translated into average of 150 IE per sample (range 140-167 IE, CV 0.13, n=600). Anaysis of 25 images by a trained classifier required 3 minutes including the training of the classifier. CONCLUSIONS: The data demonstrate feasibility of utilisation of a trainable algorithm for rapid analysis of multiple islet images. Development of more advanced algorithm is under way. Supported by Health Ministry, Czech Republic NT/13099 (en)
|
| Title
| - Assessment of a Novel Trainable Algorithm for Automated Segmentation of Multiple Islet Images.
- Assessment of a Novel Trainable Algorithm for Automated Segmentation of Multiple Islet Images. (en)
|
| skos:prefLabel
| - Assessment of a Novel Trainable Algorithm for Automated Segmentation of Multiple Islet Images.
- Assessment of a Novel Trainable Algorithm for Automated Segmentation of Multiple Islet Images. (en)
|
| skos:notation
| - RIV/68407700:21230/14:00217904!RIV15-GA0-21230___
|
| http://linked.open...avai/riv/aktivita
| |
| http://linked.open...avai/riv/aktivity
| |
| http://linked.open...vai/riv/dodaniDat
| |
| http://linked.open...aciTvurceVysledku
| |
| http://linked.open.../riv/druhVysledku
| |
| http://linked.open...iv/duvernostUdaju
| |
| http://linked.open...titaPredkladatele
| |
| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/14:00217904
|
| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - Langerhans islet; linear classifier; IE; segmentation (en)
|
| http://linked.open.../riv/klicoveSlovo
| |
| http://linked.open...ontrolniKodProRIV
| |
| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
| |
| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...vavai/riv/projekt
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...iv/tvurceVysledku
| - Berková, Z.
- Kybic, Jan
- Saudek, F.
- Zacharovová, K.
- Švihlík, Jan
- Cahová, M.
- Kříž, J.
- Girman, P.
- Habart, D.
- Papáčková, Z.
|
| http://localhost/t...ganizacniJednotka
| |
![[RDF Data]](/fct/images/sw-rdf-blue.png)
![[cxml]](/fct/images/cxml_doc.png)
![[csv]](/fct/images/csv_doc.png)
![[text]](/fct/images/ntriples_doc.png)
![[turtle]](/fct/images/n3turtle_doc.png)
![[ld+json]](/fct/images/jsonld_doc.png)
![[rdf+json]](/fct/images/json_doc.png)
![[rdf+xml]](/fct/images/xml_doc.png)
![[atom+xml]](/fct/images/atom_doc.png)
![[html]](/fct/images/html_doc.png)