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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Expert systems with applications Ročník 299; s. 130302
Hlavní autori: Curci, Giorgia, Casti, Paola, Sala, Luca, Brescia, Marcella, Cascarano, Pasquale, D’Orazio, Michele, Filippi, Joanna, Antonelli, Gianni, Mencattini, Arianna, Mastrangeli, Massimo, van Meer, Berend J., Martinelli, Eugenio
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.03.2026
Predmet:
Contraction analysis
Data restoration
Time-lapse microscopy
Variational autoencoders
Variational autoencoders
Time-lapse microscopy
Contraction analysis
Data restoration
ISSN:0957-4174
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
ISSN:0957-4174
DOI:10.1016/j.eswa.2025.130302