Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F16%3A43901595" target="_blank" >RIV/60461373:22340/16:43901595 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985556:_____/16:00465945 RIV/68407700:21230/16:00304584 RIV/00023001:_____/16:00060180

Výsledek na webu

<a href="http://docserver.ingentaconnect.com.ezproxy.vscht.cz/deliver/connect/cog/09636897/v25n12/s5.pdf?expires=1486165890&id=89827820&titleid=5476&accname=Institute+of+Chemical+Technology%2C+Prague&checksum=42223F3A4B4E1B81746F54F2DC1FF32A" target="_blank" >http://docserver.ingentaconnect.com.ezproxy.vscht.cz/deliver/connect/cog/09636897/v25n12/s5.pdf?expires=1486165890&id=89827820&titleid=5476&accname=Institute+of+Chemical+Technology%2C+Prague&checksum=42223F3A4B4E1B81746F54F2DC1FF32A</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3727/096368916X692005" target="_blank" >10.3727/096368916X692005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

Popis výsledku v původním jazyce

Clinical islet transplantation programs rely on the capacities of individual centers to quantify isolated islets. Current computer-assisted methods require input from human operators. Here, we describe two machine learning algorithms for islet quantification, the trainable islet algorithm (TIA) and the non-trainable purity algorithm (NPA). These algorithms automatically segment pancreatic islets and exocrine tissue on microscopic images in order to count individual islets and calculate islet volume and purity. References for islet counts and volumes were generated by the fully manual segmentation (FMS) method, which was validated against the internal DNA standard. References for islet purity were generated via the expert visual assessment (EVA) method, which was validated against the FMS method. The TIA is intended to automatically evaluate micrographs of isolated islets from future donors, after being trained on micrographs from a limited number of past donors. Its training ability was first evaluated on 46 images from four donors. The pixel-to-pixel comparison, binary statistics, and islet DNA concentration indicated that the TIA was successfully trained, regardless of the color differences of the original images. Next, the TIA trained on the four donors was validated on an additional 36 images from nine independent donors. The TIA was fast (67 sec/image), correlated very well with the FMS method (R2 = 1.00 and 0.92 for islet volume and islet count, respectively), and had small REs (0.06 and 0.07 for islet volume and islet count, respectively). Validation of the NPA against the EVA method using 70 images from 12 donors revealed that the NPA had a reasonable speed (69 sec/image), an acceptable RE (0.14), and correlated well with the EVA method (R2 = 0.88). Our results demonstrate that a fully automated analysis of clinical-grade micrographs of isolated pancreatic islets is feasible. The algorithms described herein will be freely available as a Fiji platform plugin.

Název v anglickém jazyce

Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

Popis výsledku anglicky

Clinical islet transplantation programs rely on the capacities of individual centers to quantify isolated islets. Current computer-assisted methods require input from human operators. Here, we describe two machine learning algorithms for islet quantification, the trainable islet algorithm (TIA) and the non-trainable purity algorithm (NPA). These algorithms automatically segment pancreatic islets and exocrine tissue on microscopic images in order to count individual islets and calculate islet volume and purity. References for islet counts and volumes were generated by the fully manual segmentation (FMS) method, which was validated against the internal DNA standard. References for islet purity were generated via the expert visual assessment (EVA) method, which was validated against the FMS method. The TIA is intended to automatically evaluate micrographs of isolated islets from future donors, after being trained on micrographs from a limited number of past donors. Its training ability was first evaluated on 46 images from four donors. The pixel-to-pixel comparison, binary statistics, and islet DNA concentration indicated that the TIA was successfully trained, regardless of the color differences of the original images. Next, the TIA trained on the four donors was validated on an additional 36 images from nine independent donors. The TIA was fast (67 sec/image), correlated very well with the FMS method (R2 = 1.00 and 0.92 for islet volume and islet count, respectively), and had small REs (0.06 and 0.07 for islet volume and islet count, respectively). Validation of the NPA against the EVA method using 70 images from 12 donors revealed that the NPA had a reasonable speed (69 sec/image), an acceptable RE (0.14), and correlated well with the EVA method (R2 = 0.88). Our results demonstrate that a fully automated analysis of clinical-grade micrographs of isolated pancreatic islets is feasible. The algorithms described herein will be freely available as a Fiji platform plugin.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Cell Transplantation

ISSN

0963-6897

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

2145-2156

Kód UT WoS článku

000390183200005

EID výsledku v databázi Scopus

2-s2.0-85007086435