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; David WIESNER; Jakub SUMBAL; Zuzana SUMBALOVÁ KOLEDOVÁ a Martin MAŠKA

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: 8.900

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/23:00130029

Organizační jednotka

Fakulta informatiky

DOI

https://doi.org/10.1109/TMI.2022.3210714

UT WoS

000907160700023

EID Scopus

2-s2.0-85139490407

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://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 = {https://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://doi.org/10.1109/TMI.2022.3210714.

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