Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + fluorescence microscopy
Abstract Advances in high-resolution live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + imaging enabled subcellular localization of early $$\hbox {Ca}^{2+}$$ Ca 2 + signaling events in T-cells and paved the way to investigate the interplay between receptors and potential target channels in $$\hbox {Ca}^{2+}$$ Ca...
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/199daa2c52834ce3beb1eb76ab2a59dc |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:199daa2c52834ce3beb1eb76ab2a59dc |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:199daa2c52834ce3beb1eb76ab2a59dc2021-12-02T14:27:45ZSpatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + fluorescence microscopy10.1038/s41598-021-87607-y2045-2322https://doaj.org/article/199daa2c52834ce3beb1eb76ab2a59dc2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87607-yhttps://doaj.org/toc/2045-2322Abstract Advances in high-resolution live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + imaging enabled subcellular localization of early $$\hbox {Ca}^{2+}$$ Ca 2 + signaling events in T-cells and paved the way to investigate the interplay between receptors and potential target channels in $$\hbox {Ca}^{2+}$$ Ca 2 + release events. The huge amount of acquired data requires efficient, ideally automated image processing pipelines, with cell localization/segmentation as central tasks. Automated segmentation in live-cell cytosolic $$\hbox {Ca}^{2+}$$ Ca 2 + imaging data is, however, challenging due to temporal image intensity fluctuations, low signal-to-noise ratio, and photo-bleaching. Here, we propose a reservoir computing (RC) framework for efficient and temporally consistent segmentation. Experiments were conducted with Jurkat T-cells and anti-CD3 coated beads used for T-cell activation. We compared the RC performance with a standard U-Net and a convolutional long short-term memory (LSTM) model. The RC-based models (1) perform on par in terms of segmentation accuracy with the deep learning models for cell-only segmentation, but show improved temporal segmentation consistency compared to the U-Net; (2) outperform the U-Net for two-emission wavelengths image segmentation and differentiation of T-cells and beads; and (3) perform on par with the convolutional LSTM for single-emission wavelength T-cell/bead segmentation and differentiation. In turn, RC models contain only a fraction of the parameters of the baseline models and reduce the training time considerably.Fatemeh HadaeghiBjörn-Philipp DiercksDaniel ScheteligFabrizio DamicelliInsa M. A. WolfRené WernerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Fatemeh Hadaeghi Björn-Philipp Diercks Daniel Schetelig Fabrizio Damicelli Insa M. A. Wolf René Werner Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + fluorescence microscopy |
| description |
Abstract Advances in high-resolution live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + imaging enabled subcellular localization of early $$\hbox {Ca}^{2+}$$ Ca 2 + signaling events in T-cells and paved the way to investigate the interplay between receptors and potential target channels in $$\hbox {Ca}^{2+}$$ Ca 2 + release events. The huge amount of acquired data requires efficient, ideally automated image processing pipelines, with cell localization/segmentation as central tasks. Automated segmentation in live-cell cytosolic $$\hbox {Ca}^{2+}$$ Ca 2 + imaging data is, however, challenging due to temporal image intensity fluctuations, low signal-to-noise ratio, and photo-bleaching. Here, we propose a reservoir computing (RC) framework for efficient and temporally consistent segmentation. Experiments were conducted with Jurkat T-cells and anti-CD3 coated beads used for T-cell activation. We compared the RC performance with a standard U-Net and a convolutional long short-term memory (LSTM) model. The RC-based models (1) perform on par in terms of segmentation accuracy with the deep learning models for cell-only segmentation, but show improved temporal segmentation consistency compared to the U-Net; (2) outperform the U-Net for two-emission wavelengths image segmentation and differentiation of T-cells and beads; and (3) perform on par with the convolutional LSTM for single-emission wavelength T-cell/bead segmentation and differentiation. In turn, RC models contain only a fraction of the parameters of the baseline models and reduce the training time considerably. |
| format |
article |
| author |
Fatemeh Hadaeghi Björn-Philipp Diercks Daniel Schetelig Fabrizio Damicelli Insa M. A. Wolf René Werner |
| author_facet |
Fatemeh Hadaeghi Björn-Philipp Diercks Daniel Schetelig Fabrizio Damicelli Insa M. A. Wolf René Werner |
| author_sort |
Fatemeh Hadaeghi |
| title |
Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + fluorescence microscopy |
| title_short |
Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + fluorescence microscopy |
| title_full |
Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + fluorescence microscopy |
| title_fullStr |
Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + fluorescence microscopy |
| title_full_unstemmed |
Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + fluorescence microscopy |
| title_sort |
spatio-temporal feature learning with reservoir computing for t-cell segmentation in live-cell $$\hbox {ca}^{2+}$$ ca 2 + fluorescence microscopy |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/199daa2c52834ce3beb1eb76ab2a59dc |
| work_keys_str_mv |
AT fatemehhadaeghi spatiotemporalfeaturelearningwithreservoircomputingfortcellsegmentationinlivecellhboxca2ca2fluorescencemicroscopy AT bjornphilippdiercks spatiotemporalfeaturelearningwithreservoircomputingfortcellsegmentationinlivecellhboxca2ca2fluorescencemicroscopy AT danielschetelig spatiotemporalfeaturelearningwithreservoircomputingfortcellsegmentationinlivecellhboxca2ca2fluorescencemicroscopy AT fabriziodamicelli spatiotemporalfeaturelearningwithreservoircomputingfortcellsegmentationinlivecellhboxca2ca2fluorescencemicroscopy AT insamawolf spatiotemporalfeaturelearningwithreservoircomputingfortcellsegmentationinlivecellhboxca2ca2fluorescencemicroscopy AT renewerner spatiotemporalfeaturelearningwithreservoircomputingfortcellsegmentationinlivecellhboxca2ca2fluorescencemicroscopy |
| _version_ |
1718391294630297600 |