Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00130029" target="_blank" >RIV/00216224:14330/23:00130029 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/TMI.2022.3210714" target="_blank" >https://doi.org/10.1109/TMI.2022.3210714</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TMI.2022.3210714" target="_blank" >10.1109/TMI.2022.3210714</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy

Popis výsledku v původním jazyce

We present an automated and deep-learningbased workflow to quantitatively analyze the spatiotemporal development of mammary epithelial organoids in twodimensional time-lapse (2D+t) sequences acquired using a brightfield microscope at high resolution. It involves a convolutional neural network (U-Net), purposely trained using computer-generated bioimage data created by a conditional generative adversarial network (pix2pixHD), to infer semantic segmentation, adaptive morphological filtering to identify organoid instances, and a shape-similarity-constrained, instance-segmentation-correcting tracking procedure to reliably cherry-pick the organoid instances of interest in time. By validating it using real 2D+t sequences of mouse mammary epithelial organoids of morphologically different phenotypes, we clearly demonstrate that the workflow achieves reliable segmentation and tracking performance, providing a reproducible and laborless alternative to manual analyses of the acquired bioimage data.

Název v anglickém jazyce

Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy

Popis výsledku anglicky

We present an automated and deep-learningbased workflow to quantitatively analyze the spatiotemporal development of mammary epithelial organoids in twodimensional time-lapse (2D+t) sequences acquired using a brightfield microscope at high resolution. It involves a convolutional neural network (U-Net), purposely trained using computer-generated bioimage data created by a conditional generative adversarial network (pix2pixHD), to infer semantic segmentation, adaptive morphological filtering to identify organoid instances, and a shape-similarity-constrained, instance-segmentation-correcting tracking procedure to reliably cherry-pick the organoid instances of interest in time. By validating it using real 2D+t sequences of mouse mammary epithelial organoids of morphologically different phenotypes, we clearly demonstrate that the workflow achieves reliable segmentation and tracking performance, providing a reproducible and laborless alternative to manual analyses of the acquired bioimage data.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/GA21-20374S" target="_blank" >GA21-20374S: Segmentace a sledování buněk se složitým tvarem</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Medical Imaging

ISSN

0278-0062

e-ISSN

1558-254X

Svazek periodika

42

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

281-290

Kód UT WoS článku

000907160700023

EID výsledku v databázi Scopus

2-s2.0-85139490407