Segmentation and Tracking of Mammary Epithelial Organoids in
Brightfield Microscopy

J 2023

Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy

HRADECKÁ, Lucia, David WIESNER, Jakub SUMBAL, Zuzana SUMBALOVÁ KOLEDOVÁ, Martin MAŠKA et. al.

Základní údaje

Originální název

Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy

Autoři

HRADECKÁ, Lucia (703 Slovensko, domácí), David WIESNER (203 Česká republika, domácí), Jakub SUMBAL (203 Česká republika, domácí), Zuzana SUMBALOVÁ KOLEDOVÁ (703 Slovensko, domácí) a Martin MAŠKA (203 Česká republika, garant, domácí)

Vydání

IEEE Transactions on Medical Imaging, 2023, 0278-0062

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 10.600 v roce 2022

Kód RIV

RIV/00216224:14330/23:00130029

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1109/TMI.2022.3210714

UT WoS

000907160700023

Klíčová slova anglicky

organoid segmentation; organoid tracking; brightfield microscopy; deep learning; image synthesis

Štítky

14110517, cbia-web

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 7. 4. 2024 22:32, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

GA21-20374S, projekt VaV

Název: Segmentace a sledování buněk se složitým tvarem

Investor: Grantová agentura ČR, Segmentace a sledování buněk se složitým tvarem

MUNI/A/1145/2021, interní kód MU

Název: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace XI. (Akronym: SV-FI MAV XI.)

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace XI.

MUNI/G/1446/2018, interní kód MU

Název: Deciphering the mechanisms of mammary epithelial branched pattern formation through iterative biological and mathematical modelling

Investor: Masarykova univerzita, Deciphering the mechanisms of mammary epithelial branched pattern formation through iterative biological and mathematical modelling, INTERDISCIPLINARY - Mezioborové výzkumné projekty

Citovat

HRADECKÁ, Lucia, David WIESNER, Jakub SUMBAL, Zuzana SUMBALOVÁ KOLEDOVÁ a Martin MAŠKA. Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy. IEEE Transactions on Medical Imaging. 2023, roč. 42, č. 1, s. 281-290. ISSN 0278-0062. Dostupné z: https://dx.doi.org/10.1109/TMI.2022.3210714.

@article{2220797,
   author = {Hradecká, Lucia and Wiesner, David and Sumbal, Jakub and Sumbalová Koledová, Zuzana and Maška, Martin},
   article_number = {1},
   doi = {http://dx.doi.org/10.1109/TMI.2022.3210714},
   keywords = {organoid segmentation; organoid tracking; brightfield microscopy; deep learning; image synthesis},
   language = {eng},
   issn = {0278-0062},
   journal = {IEEE Transactions on Medical Imaging},
   title = {Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy},
   url = {https://doi.org/10.1109/TMI.2022.3210714},
   volume = {42},
   year = {2023}
}

TY  - JOUR
ID  - 2220797
AU  - Hradecká, Lucia - Wiesner, David - Sumbal, Jakub - Sumbalová Koledová, Zuzana - Maška, Martin
PY  - 2023
TI  - Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy
JF  - IEEE Transactions on Medical Imaging
VL  - 42
IS  - 1
SP  - 281-290
EP  - 281-290
SN  - 02780062
KW  - organoid segmentation
KW  - organoid tracking
KW  - brightfield microscopy
KW  - deep learning
KW  - image synthesis
UR  - https://doi.org/10.1109/TMI.2022.3210714
N2  - 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.
ER  -

HRADECKÁ, Lucia, David WIESNER, Jakub SUMBAL, Zuzana SUMBALOVÁ KOLEDOVÁ a Martin MAŠKA. Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy. \textit{IEEE Transactions on Medical Imaging}. 2023, roč.~42, č.~1, s.~281-290. ISSN~0278-0062. Dostupné z: https://dx.doi.org/10.1109/TMI.2022.3210714.

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