VAE-MOTION: A deep generative model for cardiomyocyte contractility analysis for improving drug efficacy evaluation
Deep learning has proven to be one of the most effective methods in analyzing biological images to extract parameters fundamental for studying physiological functions and pathological conditions. In particular, when coupled with time-lapse microscopy (TLM), deep learning proves particularly effectiv...
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| Vydáno v: | Expert systems with applications Ročník 299; s. 130302 |
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| Jazyk: | angličtina |
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Elsevier Ltd
01.03.2026
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| ISSN: | 0957-4174 |
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| Abstract | Deep learning has proven to be one of the most effective methods in analyzing biological images to extract parameters fundamental for studying physiological functions and pathological conditions. In particular, when coupled with time-lapse microscopy (TLM), deep learning proves particularly effective in studying behaviors involving temporal dynamics. However, TLM videos are often affected by experimental noise and setup limitations, which can lead to inaccurate and poorly reproducible results. Taking advantage of the variational and generative capabilities of Variational Autoencoders (VAEs), we propose VAE-MOTION, a deep learning-based model for the analysis of cardiac contractile dynamics. By incorporating a temporal encoder into its architecture, our model allows the restoration of video quality by removing noise or increasing resolution, while simultaneously extracting accurate contraction-related signals from the latent space. The generation of synthetic videos allowed extensive training of VAE-MOTION, which subsequently validated on real videos from two different cardiac tissue models: 2D monolayers and 3D microtissues. VAE-MOTION was compared to two gold-standard methods in extracting contraction parameters relevant to drug efficacy or toxicity studies, demonstrating its potential for analyzing temporal dynamics in a given phenomenon or process. |
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| AbstractList | Deep learning has proven to be one of the most effective methods in analyzing biological images to extract parameters fundamental for studying physiological functions and pathological conditions. In particular, when coupled with time-lapse microscopy (TLM), deep learning proves particularly effective in studying behaviors involving temporal dynamics. However, TLM videos are often affected by experimental noise and setup limitations, which can lead to inaccurate and poorly reproducible results. Taking advantage of the variational and generative capabilities of Variational Autoencoders (VAEs), we propose VAE-MOTION, a deep learning-based model for the analysis of cardiac contractile dynamics. By incorporating a temporal encoder into its architecture, our model allows the restoration of video quality by removing noise or increasing resolution, while simultaneously extracting accurate contraction-related signals from the latent space. The generation of synthetic videos allowed extensive training of VAE-MOTION, which subsequently validated on real videos from two different cardiac tissue models: 2D monolayers and 3D microtissues. VAE-MOTION was compared to two gold-standard methods in extracting contraction parameters relevant to drug efficacy or toxicity studies, demonstrating its potential for analyzing temporal dynamics in a given phenomenon or process. |
| ArticleNumber | 130302 |
| Author | Antonelli, Gianni Brescia, Marcella Filippi, Joanna Casti, Paola Mencattini, Arianna Martinelli, Eugenio Mastrangeli, Massimo Curci, Giorgia Sala, Luca van Meer, Berend J. D’Orazio, Michele Cascarano, Pasquale |
| Author_xml | – sequence: 1 givenname: Giorgia surname: Curci fullname: Curci, Giorgia email: giorgia.curci@uniroma2.it organization: Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy – sequence: 2 givenname: Paola surname: Casti fullname: Casti, Paola email: casti@ing.uniroma2.it organization: Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy – sequence: 3 givenname: Luca surname: Sala fullname: Sala, Luca email: luca.sala@auxologico.it organization: Department of Biotechnology and Biosciences, University of Milano – Bicocca, Milan, Italy – sequence: 4 givenname: Marcella surname: Brescia fullname: Brescia, Marcella email: M.Dias_Brescia@lumc.nl organization: Department of Anatomy & Embryology, Leiden University Medical Center, The Netherlands – sequence: 5 givenname: Pasquale surname: Cascarano fullname: Cascarano, Pasquale email: pasquale.cascarano2@unibo.it organization: Department of the Arts, University of Bologna, Bologna, Italy – sequence: 6 givenname: Michele surname: D’Orazio fullname: D’Orazio, Michele email: michele.d.orazio@uniroma2.it organization: Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy – sequence: 7 givenname: Joanna surname: Filippi fullname: Filippi, Joanna email: filippi@ing.uniroma2.it organization: Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy – sequence: 8 givenname: Gianni surname: Antonelli fullname: Antonelli, Gianni email: g.antonelli@ing.uniroma2.it organization: Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy – sequence: 9 givenname: Arianna surname: Mencattini fullname: Mencattini, Arianna email: mencattini@ing.uniroma2.it organization: Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy – sequence: 10 givenname: Massimo surname: Mastrangeli fullname: Mastrangeli, Massimo email: M.Mastrangeli@tudelft.nl organization: Microelectronics Deparment, Delft University of Technology, Delft, The Netherlands – sequence: 11 givenname: Berend J. surname: van Meer fullname: van Meer, Berend J. email: berend.van.meer@demcon.com organization: Department of Anatomy & Embryology, Leiden University Medical Center, The Netherlands – sequence: 12 givenname: Eugenio surname: Martinelli fullname: Martinelli, Eugenio email: martinelli@ing.uniroma2.it organization: Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy |
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| Keywords | Variational autoencoders Time-lapse microscopy Contraction analysis Data restoration |
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publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2023.3239594 |
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