CLIJ: GPU-accelerated image processing for everyone

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F20%3A10245447" target="_blank" >RIV/61989100:27740/20:10245447 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1038/s41592-019-0650-1" target="_blank" >https://doi.org/10.1038/s41592-019-0650-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41592-019-0650-1" target="_blank" >10.1038/s41592-019-0650-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

CLIJ: GPU-accelerated image processing for everyone

Popis výsledku v původním jazyce

Modern microscopy generates staggering amounts of multidimensional image data that place increasing demands on processing flexibility and efficiency. One way to speed up image processing is to exploit the parallel processing capabilities of graphics processing units (GPU). Recently, GPU-acceleration was used in specific image-processing tasks such as reconstruction, image quality determination, image restoration, segmentation or visualization. However, in such tools, GPU code is fulfilling one specific purpose and is not intended to be reused in other contexts. By contrast, most common image processing tasks are solved by building flexible workflows consisting of simple operations in widely used tools such as ImageJ and Fiji. Most of these operations were however programmed at a time when GPUs were not commonly used for general-purpose processing. Therefore, typical workflows consisting of core ImageJ operations do not take advantage of GPUs. To address this issue, we developed a flexible and reusable platform for GPU-acceleration in Fiji.

Název v anglickém jazyce

CLIJ: GPU-accelerated image processing for everyone

Popis výsledku anglicky

Modern microscopy generates staggering amounts of multidimensional image data that place increasing demands on processing flexibility and efficiency. One way to speed up image processing is to exploit the parallel processing capabilities of graphics processing units (GPU). Recently, GPU-acceleration was used in specific image-processing tasks such as reconstruction, image quality determination, image restoration, segmentation or visualization. However, in such tools, GPU code is fulfilling one specific purpose and is not intended to be reused in other contexts. By contrast, most common image processing tasks are solved by building flexible workflows consisting of simple operations in widely used tools such as ImageJ and Fiji. Most of these operations were however programmed at a time when GPUs were not commonly used for general-purpose processing. Therefore, typical workflows consisting of core ImageJ operations do not take advantage of GPUs. To address this issue, we developed a flexible and reusable platform for GPU-acceleration in Fiji.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_013%2F0001791" target="_blank" >EF16_013/0001791: IT4Innovations národní superpočítačové centrum - cesta k exascale</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Nature Methods

ISSN

1548-7091

e-ISSN

—

Svazek periodika

17

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

2

Strana od-do

5-6

Kód UT WoS článku

000508582900003

EID výsledku v databázi Scopus

2-s2.0-85075364591