Model-Based Generation of Synthetic 3D Time-Lapse Sequences of Multiple Mutually Interacting Motile Cells with Filopodia

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00101086" target="_blank" >RIV/00216224:14330/18:00101086 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-00536-8_8" target="_blank" >http://dx.doi.org/10.1007/978-3-030-00536-8_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-00536-8_8" target="_blank" >10.1007/978-3-030-00536-8_8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Model-Based Generation of Synthetic 3D Time-Lapse Sequences of Multiple Mutually Interacting Motile Cells with Filopodia

Popis výsledku v původním jazyce

Complementing collections of 3D time-lapse image data with comprehensive manual annotations is an extremely laborious and often impracticable task, which hinders objective benchmarking of bioimage analysis workflows as well as training of widespread deep-learning-based approaches. In this paper, we present a novel simulation system capable of generating synthetic 3D time-lapse sequences of multiple mutually interacting cells with filopodial protrusions, accompanied by inherently generated reference annotations, in order to stimulate the development of fully 3D bioimage analysis workflows for filopodium segmentation and tracking in complex scenarios with multiple mutually interacting cells. The system integrates its predecessor, which was designed for single-cell, collision-unaware scenarios only, with proactive, mechanics-based handling of collisions between multiple filopodia, multiple cell bodies, or their combinations. We demonstrate its potential on two generated 3D time-lapse sequences of multiple lung cancer cells with curvilinear filopodia, which visually resemble confocal fluorescence microscopy image data.

Název v anglickém jazyce

Model-Based Generation of Synthetic 3D Time-Lapse Sequences of Multiple Mutually Interacting Motile Cells with Filopodia

Popis výsledku anglicky

Complementing collections of 3D time-lapse image data with comprehensive manual annotations is an extremely laborious and often impracticable task, which hinders objective benchmarking of bioimage analysis workflows as well as training of widespread deep-learning-based approaches. In this paper, we present a novel simulation system capable of generating synthetic 3D time-lapse sequences of multiple mutually interacting cells with filopodial protrusions, accompanied by inherently generated reference annotations, in order to stimulate the development of fully 3D bioimage analysis workflows for filopodium segmentation and tracking in complex scenarios with multiple mutually interacting cells. The system integrates its predecessor, which was designed for single-cell, collision-unaware scenarios only, with proactive, mechanics-based handling of collisions between multiple filopodia, multiple cell bodies, or their combinations. We demonstrate its potential on two generated 3D time-lapse sequences of multiple lung cancer cells with curvilinear filopodia, which visually resemble confocal fluorescence microscopy image data.

Druh

D - Stať ve sborníku

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/GJ16-03909Y" target="_blank" >GJ16-03909Y: Vývoj spolehlivých metod pro automatizovanou kvantitativní charakterizaci buněčné motility ve fluorescenční mikroskopii</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Simulation and Synthesis in Medical Imaging

ISBN

9783030005351

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

71-79

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Granada, Spain

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000477752900008