Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Segmentation and Tracking of Mammary Epithelial Organoids in Brightfield Microscopy
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/GA21-20374S" target="_blank" >GA21-20374S: Segmentation and tracking of cells with complex shapes</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Medical Imaging
ISSN
0278-0062
e-ISSN
1558-254X
Volume of the periodical
42
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
281-290
UT code for WoS article
000907160700023
EID of the result in the Scopus database
2-s2.0-85139490407