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...
Saved in:
| Published in: | Expert systems with applications Vol. 299; p. 130302 |
|---|---|
| Main Authors: | , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.03.2026
|
| Subjects: | |
| ISSN: | 0957-4174 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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. |
|---|---|
| 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 |
| BookMark | eNp9kLtOwzAYhT0UiRZ4ASa_QIKvuSCWqipQqdClsFrG_l25SuLKToPy9rSUmeks5zs6-mZo0oUOELqnJKeEFg_7HNK3zhlhMqeccMImaEpqWWaCluIazVLaE0JLQsopSp_zZfa22a427494ji3AAe-gg6h7PwBug4UGuxCx0dH60I7BjD1gE7o-atP7xvcj1p1uxuTTb9G3hxgG3-2wjccdBue80WbEMOjmeFoN3S26crpJcPeXN-jjebldvGbrzctqMV9nhkneZ4brSggDQoqicsQQa7-kFdxWzkItgVioGCUll1BDIU3JpBY1cwXjNS215jeIXXZNDClFcOoQfavjqChRZ1Vqr86q1FmVuqg6QU8XCE7PBg9RJeOhM2B9BNMrG_x_-A9yw3gC |
| Cites_doi | 10.1007/BF01420984 10.1109/TIP.2017.2662206 10.1016/j.patter.2021.100261 10.1109/TPAMI.2021.3116668 10.18063/ijb.v7i3.370 10.1016/B978-0-12-824349-7.00015-3 10.1088/0957-0233/8/12/007 10.1038/s41598-022-12364-5 10.1109/JSEN.2024.3463959 10.3389/fbioe.2024.1367141 10.1039/C7LC00512A 10.1016/B978-0-12-336156-1.50061-6 10.1016/j.eswa.2024.125157 10.1109/TPAMI.2022.3215571 10.1145/3422622 10.5334/jors.334 10.1117/1.JBO.25.8.086502 10.1038/s41598-019-42475-5 10.1371/journal.pcbi.1006235 10.1088/1748-6041/10/3/034006 10.1016/j.ijthermalsci.2012.11.009 10.1016/j.media.2021.102124 10.1161/CIRCRESAHA.121.318183 10.1109/TMI.2012.2220375 10.1109/TIM.2023.3303498 10.1016/j.eswa.2023.120861 10.1016/j.neunet.2020.07.025 10.5334/jors.bl 10.1093/ajh/4.2.185S 10.1214/aoms/1177729586 10.1007/s00521-022-07953-4 10.1016/S0006-3495(02)75489-1 10.1007/s00521-020-05226-6 10.1038/s42003-023-04585-9 10.1038/s41598-020-72605-3 10.1007/s11227-017-2080-0 10.1038/s42256-022-00503-6 10.1088/1758-5090/ad4ba1 10.1016/j.eswa.2023.122252 10.1109/OJEMB.2024.3377461 10.1038/nature14539 10.1214/aoms/1177729694 10.1016/j.crmeth.2021.100044 10.1007/s00018-024-05231-1 10.1016/j.bspc.2024.106598 10.1109/JSEN.2020.3036005 10.1007/3-540-45103-X_50 10.1190/IGC2018-113 10.1242/dev.143438 10.1038/415198a 10.1109/TBME.2023.3239594 |
| ContentType | Journal Article |
| Copyright | 2025 The Author(s) |
| Copyright_xml | – notice: 2025 The Author(s) |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.eswa.2025.130302 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| ExternalDocumentID | 10_1016_j_eswa_2025_130302 S095741742503917X |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 6I. 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AAEDT AAEDW AAFTH AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABJNI ABMAC ABMVD ABUCO ABUFD ACDAQ ACGFS ACHRH ACLOT ACNTT ACRLP ACVFH ACZNC ADBBV ADCNI ADEZE ADTZH AEBSH AECPX AEIPS AEKER AENEX AEUPX AFJKZ AFPUW AFTJW AGHFR AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALEQD ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD APLSM APXCP AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFKBS EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- ~HD 29G 9DU AAAKG AAQXK AAYXX ABKBG ABWVN ABXDB ACNNM ACRPL ADJOM ADMUD ADNMO AGQPQ ASPBG AVWKF AZFZN CITATION EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET WUQ XPP ZMT |
| ID | FETCH-LOGICAL-c253t-c3a844ce45468f0c0ddb5d43d8fde95e0de8210735e9e65c725a492f623917aa3 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001615058200007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Thu Nov 27 00:49:09 EST 2025 Wed Dec 10 14:26:12 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Variational autoencoders Time-lapse microscopy Contraction analysis Data restoration |
| Language | English |
| License | This is an open access article under the CC BY license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c253t-c3a844ce45468f0c0ddb5d43d8fde95e0de8210735e9e65c725a492f623917aa3 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.eswa.2025.130302 |
| ParticipantIDs | crossref_primary_10_1016_j_eswa_2025_130302 elsevier_sciencedirect_doi_10_1016_j_eswa_2025_130302 |
| PublicationCentury | 2000 |
| PublicationDate | 2026-03-01 |
| PublicationDateYYYYMMDD | 2026-03-01 |
| PublicationDate_xml | – month: 03 year: 2026 text: 2026-03-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2026 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Harmand, Pellé, Poncet, Shevchuk (b0110) 2013; 67 Zhang, Zuo, Chen, Meng, Zhang (b0295) 2017; 26 http://arxiv.org/abs/1412.6980. Comes, Filippi, Mencattini, Casti, Cerrato, Sauvat, Vacchelli, De Ninno, Di Giuseppe, D’Orazio, Mattei, Schiavoni, Businaro, Di Natale, Kroemer, Martinelli (b0070) 2021; 33 Huang, Dabiri, Gharib (b0125) 1997; 8 Comes, Casti, Mencattini, Di Giuseppe, Mermet-Meillon, De Ninno, Parrini, Businaro, Di Natale, Martinelli (b0065) 2019; 9 (pp. 363–370). https://doi.org/10.1007/3-540-45103-X_50. Yang, Xiao, Lv, Zhang, Liu (b0280) 2024; 7 Kim, Wang, Lock, Nash, Fleischer, Wang, Fine, Vunjak-Novakovic (b0140) 2024; 5 Qian, Lv, Lv, Gu, Wang, Zhang, Gupta (b0205) 2021; 21 Thielicke, Sonntag (b0260) 2021; 9 Zhang, Aleman, Arneri, Bersini, Piraino, Shin, Dokmeci, Khademhosseini (b0305) 2015; 10 Li, Nie, Morimoto, Takeuchi (b0175) 2024; 16 Rong, OuYang, Sun (b0220) 2022; 2022 Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio (b0105) 2020; 63 Mencattini, Spalloni, Casti, Comes, Di Giuseppe, Antonelli, D’Orazio, Filippi, Corsi, Isambert, Di Natale, Longone, Martinelli (b0195) 2021; 2 461–464. https://doi.org/10.1190/IGC2018-113. Bond-Taylor, Leach, Long, Willcocks (b0035) 2022; 44 Sala, van Meer, Tertoolen, Bakkers, Bellin, Davis, Denning, Dieben, Eschenhagen, Giacomelli, Grandela, Hansen, Holman, Jongbloed, Kamel, Koopman, Lachaud, Mannhardt, Mol, Burton (b0225) 2017 Antonelli, Camera, Mencattini, Casciati, Tanori, Zambotti, Casti, Curci, Filippi, D’Orazio, Merla, Martinelli (b0010) 2024; 24 Cascarano, Comes, Mencattini, Parrini, Piccolomini, Martinelli (b0045) 2021; 72 D’Orazio, Murdocca, Mencattini, Casti, Filippi, Antonelli, Di Giuseppe, Comes, Di Natale, Sangiuolo, Martinelli (b0080) 2022; 12 LeCun, Bengio, Hinton (b0160) 2015; 521 Harrison, Baker (b0115) 2018; 14 Bers (b0030) 2002; 415 Comes, Filippi, Mencattini, Corsi, Casti, De Ninno, Di Giuseppe, D’Orazio, Ghibelli, Mattei, Schiavoni, Businaro, Di Natale, Martinelli (b0075) 2020; 10 Kullback, Leibler (b0155) 1951; 22 Rojas (b0210) 1996 . Soelistyo, Vallardi, Charras, Lowe (b0245) 2022; 4 Strbkova, Carson, Vincent, Vesely, Chmelik (b0255) 2020; 25 Ascione, Caserta, Perris, Guido (b0015) 2014; 38 Lee, Kim, Yun, Bae, Park, Jeon, Jang, Lee, Lee (b0165) 2024; 12 Li, Sundaram, Hu, Lou, Sanchez, McDonald, Agarwal, Chen, Bifano (b0170) 2023; 70 Tian, Fei, Zheng, Xu, Zuo, Lin (b0270) 2020; 131 Barron, Fleet, Beauchemin (b0020) 1994; 12 Mencattini, D’Orazio, Casti, Comes, Di Giuseppe, Antonelli, Filippi, Corsi, Ghibelli, Veith, Di Natale, Parrini, Martinelli (b0190) 2023; 6 Kingma, D. P., & Welling, M. (2013). Thielicke, Stamhuis (b0265) 2014; 2 Yu, S., & Ma, J. (2018). Deep learning for denoising. (pp. 129–162). Elsevier. https://doi.org/10.1016/B978-0-12-824349-7.00015-3. Scalzo, Afonso, da Fonseca, Jesus, Alves, Mendonça, Teixeira, Biagi, Cruvinel, Santos, Miranda, Marques, Mesquita, Kushmerick, Campagnole-Santos, Agero, Guatimosim (b0235) 2021; 1 Casti, Cardarelli, Comes, D’Orazio, Filippi, Antonelli, Mencattini, Di Natale, Martinelli (b0050) 2023; 232 Giacomelli, Bellin, Sala, van Meer, Tertoolen, Orlova, Mummery (b0100) 2017 Chen, Zhou, Li, Chen, Wang, Li (b0060) 2024; 258 Zheng, D., Zhang, X., Ma, K., & Bao, C. (2022). Agrawal, Aung, Varghese (b0005) 2017; 17 He, Zhang, Ren, Sun (b0120) 2016; 2016 Zhang, Yang, Lin, Ji, Gupta (b0300) 2018 Wang, Messi, Delbono (b0275) 2002; 82 http://arxiv.org/abs/1502.03167. Schwinger, Böhm, Erdmann (b0240) 1991; 4 Zuiderveld, K. (1994). Contrast Limited Adaptive Histogram Equalization. In Prakosa, Sermesant, Delingette, Marchesseau, Saloux, Allain, Villain, Ayache (b0200) 2013; 32 (pp. 474–485). Elsevier. https://doi.org/10.1016/B978-0-12-336156-1.50061-6. Ioffe, S., & Szegedy, C. (2015). Benezeth, Krishnamoorthy, Botina Monsalve, Nakamura, Gomez, Mitéran (b0025) 2024; 96 Yun, Kim, Kim, Yoo (b0290) 2024; 238 Filippi, Corsi, Casti, Antonelli, D’Orazio, Capradossi, Capuano, Curci, Ghibelli, Mencattini, Martinelli (b0095) 2024; 2023 Campostrini, Windt, van Meer, Bellin, Mummery (b0040) 2021; 128 Kingma, D. P., & Ba, J. (2014). Mencattini, Casti, D’Orazio, Antonelli, Filippi, Martinelli (b0185) 2023; 72 Ehrhardt, J., & Wilms, M. (2022). Autoencoders and variational autoencoders in medical image analysis. In Celard, Iglesias, Sorribes-Fdez, Romero, Vieira, Borrajo (b0055) 2023; 35 Farnebäck, G. (2003). Stiefbold, Zhang, Wan (b0250) 2024; 81 Jifara, Jiang, Rho, Cheng, Liu (b0135) 2019; 75 Lin, Clark, Birke, Schonborn, Trigoni, Roberts (b0180) 2020 Robbins, Monro (b0215) 1951; 22 Sami, Mobin (b0230) 2019; 2019 Sala (10.1016/j.eswa.2025.130302_b0225) 2017 Lee (10.1016/j.eswa.2025.130302_b0165) 2024; 12 Huang (10.1016/j.eswa.2025.130302_b0125) 1997; 8 Strbkova (10.1016/j.eswa.2025.130302_b0255) 2020; 25 Celard (10.1016/j.eswa.2025.130302_b0055) 2023; 35 Comes (10.1016/j.eswa.2025.130302_b0065) 2019; 9 10.1016/j.eswa.2025.130302_b0130 Yang (10.1016/j.eswa.2025.130302_b0280) 2024; 7 Zhang (10.1016/j.eswa.2025.130302_b0305) 2015; 10 10.1016/j.eswa.2025.130302_b0090 Jifara (10.1016/j.eswa.2025.130302_b0135) 2019; 75 D’Orazio (10.1016/j.eswa.2025.130302_b0080) 2022; 12 Qian (10.1016/j.eswa.2025.130302_b0205) 2021; 21 Campostrini (10.1016/j.eswa.2025.130302_b0040) 2021; 128 Goodfellow (10.1016/j.eswa.2025.130302_b0105) 2020; 63 Zhang (10.1016/j.eswa.2025.130302_b0300) 2018 Bers (10.1016/j.eswa.2025.130302_b0030) 2002; 415 Schwinger (10.1016/j.eswa.2025.130302_b0240) 1991; 4 Harrison (10.1016/j.eswa.2025.130302_b0115) 2018; 14 Soelistyo (10.1016/j.eswa.2025.130302_b0245) 2022; 4 Chen (10.1016/j.eswa.2025.130302_b0060) 2024; 258 Rong (10.1016/j.eswa.2025.130302_b0220) 2022; 2022 Li (10.1016/j.eswa.2025.130302_b0175) 2024; 16 Casti (10.1016/j.eswa.2025.130302_b0050) 2023; 232 Kim (10.1016/j.eswa.2025.130302_b0140) 2024; 5 10.1016/j.eswa.2025.130302_b0285 Scalzo (10.1016/j.eswa.2025.130302_b0235) 2021; 1 Li (10.1016/j.eswa.2025.130302_b0170) 2023; 70 10.1016/j.eswa.2025.130302_b0085 Yun (10.1016/j.eswa.2025.130302_b0290) 2024; 238 Benezeth (10.1016/j.eswa.2025.130302_b0025) 2024; 96 Comes (10.1016/j.eswa.2025.130302_b0070) 2021; 33 Zhang (10.1016/j.eswa.2025.130302_b0295) 2017; 26 Giacomelli (10.1016/j.eswa.2025.130302_b0100) 2017 Cascarano (10.1016/j.eswa.2025.130302_b0045) 2021; 72 Sami (10.1016/j.eswa.2025.130302_b0230) 2019; 2019 Bond-Taylor (10.1016/j.eswa.2025.130302_b0035) 2022; 44 Stiefbold (10.1016/j.eswa.2025.130302_b0250) 2024; 81 Filippi (10.1016/j.eswa.2025.130302_b0095) 2024; 2023 He (10.1016/j.eswa.2025.130302_b0120) 2016; 2016 10.1016/j.eswa.2025.130302_b0315 Mencattini (10.1016/j.eswa.2025.130302_b0190) 2023; 6 Lin (10.1016/j.eswa.2025.130302_b0180) 2020 Mencattini (10.1016/j.eswa.2025.130302_b0185) 2023; 72 Robbins (10.1016/j.eswa.2025.130302_b0215) 1951; 22 Tian (10.1016/j.eswa.2025.130302_b0270) 2020; 131 Wang (10.1016/j.eswa.2025.130302_b0275) 2002; 82 10.1016/j.eswa.2025.130302_b0310 10.1016/j.eswa.2025.130302_b0150 Mencattini (10.1016/j.eswa.2025.130302_b0195) 2021; 2 Antonelli (10.1016/j.eswa.2025.130302_b0010) 2024; 24 LeCun (10.1016/j.eswa.2025.130302_b0160) 2015; 521 Barron (10.1016/j.eswa.2025.130302_b0020) 1994; 12 Agrawal (10.1016/j.eswa.2025.130302_b0005) 2017; 17 Thielicke (10.1016/j.eswa.2025.130302_b0265) 2014; 2 Ascione (10.1016/j.eswa.2025.130302_b0015) 2014; 38 Comes (10.1016/j.eswa.2025.130302_b0075) 2020; 10 Harmand (10.1016/j.eswa.2025.130302_b0110) 2013; 67 Rojas (10.1016/j.eswa.2025.130302_b0210) 1996 Thielicke (10.1016/j.eswa.2025.130302_b0260) 2021; 9 10.1016/j.eswa.2025.130302_b0145 Kullback (10.1016/j.eswa.2025.130302_b0155) 1951; 22 Prakosa (10.1016/j.eswa.2025.130302_b0200) 2013; 32 |
| References_xml | – volume: 33 start-page: 3671 year: 2021 end-page: 3689 ident: b0070 article-title: Multi-scale generative adversarial network for improved evaluation of cell–cell interactions observed in organ-on-chip experiments publication-title: Neural Computing and Applications – reference: Ioffe, S., & Szegedy, C. (2015). – volume: 14 year: 2018 ident: b0115 article-title: The impact of temporal sampling resolution on parameter inference for biological transport models publication-title: PLOS Computational Biology – volume: 238 year: 2024 ident: b0290 article-title: Kernel adaptive memory network for blind video super-resolution publication-title: Expert Systems with Applications – volume: 9 start-page: 12 year: 2021 ident: b0260 article-title: Particle Image Velocimetry for MATLAB: Accuracy and enhanced algorithms in PIVlab publication-title: Journal of Open Research Software – reference: Kingma, D. P., & Welling, M. (2013). – volume: 4 start-page: 636 year: 2022 end-page: 644 ident: b0245 article-title: Learning biophysical determinants of cell fate with deep neural networks publication-title: Nature Machine Intelligence – volume: 12 start-page: 43 year: 1994 end-page: 77 ident: b0020 article-title: Performance of optical flow techniques publication-title: International Journal of Computer Vision – volume: 32 start-page: 99 year: 2013 end-page: 109 ident: b0200 article-title: Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images publication-title: IEEE Transactions on Medical Imaging – reference: (pp. 474–485). Elsevier. https://doi.org/10.1016/B978-0-12-336156-1.50061-6. – reference: Kingma, D. P., & Ba, J. (2014). – volume: 232 year: 2023 ident: b0050 article-title: S3-VAE: A novel Supervised-Source-Separation Variational AutoEncoder algorithm to discriminate tumor cell lines in time-lapse microscopy images publication-title: Expert Systems with Applications – year: 2017 ident: b0100 article-title: Three-dimensional cardiac microtissues composed of cardiomyocytes and endothelial cells co-differentiated from human pluripotent stem cells publication-title: Development – volume: 2019 start-page: 1 year: 2019 end-page: 5 ident: b0230 article-title: A Comparative Study on Variational Autoencoders and Generative Adversarial Networks publication-title: International Conference of Artificial Intelligence and Information Technology (ICAIIT) – start-page: 1 year: 2018 end-page: 5 ident: b0300 article-title: On Definition of Deep Learning – volume: 10 start-page: 15635 year: 2020 ident: b0075 article-title: Accelerating the experimental responses on cell behaviors: A long-term prediction of cell trajectories using Social Generative Adversarial Network publication-title: Scientific Reports – volume: 1 year: 2021 ident: b0235 article-title: Dense optical flow software to quantify cellular contractility publication-title: Cell Reports Methods – volume: 8 start-page: 1427 year: 1997 end-page: 1440 ident: b0125 article-title: On errors of digital particle image velocimetry publication-title: Measurement Science and Technology – volume: 2 year: 2014 ident: b0265 article-title: PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB publication-title: Journal of Open Research Software – volume: 38 start-page: 517 year: 2014 end-page: 522 ident: b0015 article-title: Investigation of cell dynamics in vitro by time lapse microscopy and image analysis publication-title: Chemical Engineering Transactions – volume: 67 start-page: 1 year: 2013 end-page: 30 ident: b0110 article-title: Review of fluid flow and convective heat transfer within rotating disk cavities with impinging jet publication-title: International Journal of Thermal Sciences – volume: 22 start-page: 79 year: 1951 end-page: 86 ident: b0155 article-title: On Information and Sufficiency publication-title: The Annals of Mathematical Statistics – start-page: 4322 year: 2020 end-page: 4326 ident: b0180 article-title: Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model publication-title: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) – volume: 96 year: 2024 ident: b0025 article-title: Video-based heart rate estimation from challenging scenarios using synthetic video generation publication-title: Biomedical Signal Processing and Control – volume: 9 start-page: 6789 year: 2019 ident: b0065 article-title: The influence of spatial and temporal resolutions on the analysis of cell-cell interaction: A systematic study for time-lapse microscopy applications publication-title: Scientific Reports – reference: Zuiderveld, K. (1994). Contrast Limited Adaptive Histogram Equalization. In – volume: 2 year: 2021 ident: b0195 article-title: NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy publication-title: Patterns – reference: . http://arxiv.org/abs/1412.6980. – reference: . http://arxiv.org/abs/1502.03167. – volume: 258 year: 2024 ident: b0060 article-title: A high-quality self-supervised image denoising method based on SDDW-GAN and CHRNet publication-title: Expert Systems with Applications – volume: 82 start-page: 1338 year: 2002 end-page: 1344 ident: b0275 article-title: Sustained Overexpression of IGF-1 Prevents Age-Dependent Decrease in Charge Movement and Intracellular Ca2+ in Mouse Skeletal Muscle publication-title: Biophysical Journal – volume: 24 start-page: 36306 year: 2024 end-page: 36315 ident: b0010 article-title: Lab-on-Chip Label-Free Sensing System for Electroporation Based on Time-Lapse Microscopy publication-title: IEEE Sensors Journal – volume: 63 start-page: 139 year: 2020 end-page: 144 ident: b0105 article-title: Generative adversarial networks publication-title: Communications of the ACM – reference: Farnebäck, G. (2003). – volume: 10 year: 2015 ident: b0305 article-title: From cardiac tissue engineering to heart-on-a-chip: Beating challenges publication-title: Biomedical Materials – volume: 128 start-page: 775 year: 2021 end-page: 801 ident: b0040 article-title: Cardiac Tissues From Stem Cells: New Routes to Maturation and Cardiac Regeneration publication-title: Circulation Research – year: 2017 ident: b0225 publication-title: Versatile open software to quantify cardiomyocyte and cardiac muscle contraction in vitro and in vivo. – volume: 7 start-page: 370 year: 2024 ident: b0280 article-title: Fabrication and Biomedical Applications of Heart-on-a-chip publication-title: International Journal of Bioprinting – reference: Zheng, D., Zhang, X., Ma, K., & Bao, C. (2022). – reference: (pp. 363–370). https://doi.org/10.1007/3-540-45103-X_50. – volume: 72 year: 2021 ident: b0045 article-title: Recursive Deep Prior Video: A super resolution algorithm for time-lapse microscopy of organ-on-chip experiments publication-title: Medical Image Analysis – year: 1996 ident: b0210 – volume: 131 start-page: 251 year: 2020 end-page: 275 ident: b0270 article-title: Deep learning on image denoising: An overview publication-title: Neural Networks – volume: 16 year: 2024 ident: b0175 article-title: Pillar electrodes embedded in the skeletal muscle tissue for selective stimulation of biohybrid actuators with increased contractile distance publication-title: Biofabrication – volume: 2023 start-page: 71 year: 2024 ident: b0095 article-title: Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells publication-title: Eurosensors – volume: 5 start-page: 238 year: 2024 end-page: 249 ident: b0140 article-title: BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs publication-title: IEEE Open Journal of Engineering in Medicine and Biology – volume: 2016 start-page: 770 year: 2016 end-page: 778 ident: b0120 article-title: Deep Residual Learning for Image Recognition publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 75 start-page: 704 year: 2019 end-page: 718 ident: b0135 article-title: Medical image denoising using convolutional neural network: A residual learning approach publication-title: The Journal of Supercomputing – volume: 70 start-page: 2237 year: 2023 end-page: 2245 ident: b0170 article-title: Dynamic Control of Contractile Force in Engineered Heart Tissue publication-title: IEEE Transactions on Biomedical Engineering – volume: 415 start-page: 198 year: 2002 end-page: 205 ident: b0030 article-title: Cardiac excitation–contraction coupling publication-title: Nature – volume: 2022 start-page: 1 year: 2022 end-page: 14 ident: b0220 article-title: Anomaly Detection in QAR Data Using VAE-LSTM with Multihead Self-Attention Mechanism publication-title: Mobile Information Systems – volume: 12 year: 2024 ident: b0165 article-title: PIV-MyoMonitor: An accessible particle image velocimetry-based software tool for advanced contractility assessment of cardiac organoids publication-title: Frontiers in Bioengineering and Biotechnology – reference: Yu, S., & Ma, J. (2018). Deep learning for denoising. – reference: (pp. 129–162). Elsevier. https://doi.org/10.1016/B978-0-12-824349-7.00015-3. – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: b0160 article-title: Deep learning publication-title: Nature – volume: 21 start-page: 11374 year: 2021 end-page: 11381 ident: b0205 article-title: A New Approach to Polyp Detection by Pre-Processing of Images and Enhanced Faster R-CNN publication-title: IEEE Sensors Journal – reference: . – volume: 4 start-page: 185S year: 1991 end-page: 187S ident: b0240 article-title: Negative Inotropic Activity of the Calcium Antagonists Isradipine, Nifedipine, Diltiazem, and Verapamil in Diseased Human Myocardium publication-title: American Journal of Hypertension – volume: 6 start-page: 241 year: 2023 ident: b0190 article-title: Deep-Manager: A versatile tool for optimal feature selection in live-cell imaging analysis publication-title: Communications Biology – volume: 72 start-page: 1 year: 2023 end-page: 9 ident: b0185 article-title: Uncertainty-Based Feature Selection for Improved Adequacy of Dermoscopic Image Classification publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 35 start-page: 2291 year: 2023 end-page: 2323 ident: b0055 article-title: A survey on deep learning applied to medical images: From simple artificial neural networks to generative models publication-title: Neural Computing & Applications – volume: 25 year: 2020 ident: b0255 article-title: Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial–mesenchymal transition publication-title: Journal of Biomedical Optics – volume: 17 start-page: 3447 year: 2017 end-page: 3461 ident: b0005 article-title: Skeletal muscle-on-a-chip: An in vitro model to evaluate tissue formation and injury publication-title: Lab on a Chip – volume: 12 start-page: 8545 year: 2022 ident: b0080 article-title: Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response publication-title: Scientific Reports – volume: 22 start-page: 400 year: 1951 end-page: 407 ident: b0215 article-title: A Stochastic Approximation Method publication-title: The Annals of Mathematical Statistics – volume: 26 start-page: 3142 year: 2017 end-page: 3155 ident: b0295 article-title: Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising publication-title: IEEE Transactions on Image Processing – volume: 81 start-page: 197 year: 2024 ident: b0250 article-title: Engineered platforms for mimicking cardiac development and drug screening publication-title: Cellular and Molecular Life Sciences – reference: Ehrhardt, J., & Wilms, M. (2022). Autoencoders and variational autoencoders in medical image analysis. In – reference: , 461–464. https://doi.org/10.1190/IGC2018-113. – volume: 44 start-page: 7327 year: 2022 end-page: 7347 ident: b0035 article-title: Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 12 start-page: 43 issue: 1 year: 1994 ident: 10.1016/j.eswa.2025.130302_b0020 article-title: Performance of optical flow techniques publication-title: International Journal of Computer Vision doi: 10.1007/BF01420984 – volume: 26 start-page: 3142 issue: 7 year: 2017 ident: 10.1016/j.eswa.2025.130302_b0295 article-title: Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2017.2662206 – volume: 2 issue: 6 year: 2021 ident: 10.1016/j.eswa.2025.130302_b0195 article-title: NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy publication-title: Patterns doi: 10.1016/j.patter.2021.100261 – volume: 44 start-page: 7327 issue: 11 year: 2022 ident: 10.1016/j.eswa.2025.130302_b0035 article-title: Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2021.3116668 – volume: 7 start-page: 370 issue: 3 year: 2024 ident: 10.1016/j.eswa.2025.130302_b0280 article-title: Fabrication and Biomedical Applications of Heart-on-a-chip publication-title: International Journal of Bioprinting doi: 10.18063/ijb.v7i3.370 – ident: 10.1016/j.eswa.2025.130302_b0085 doi: 10.1016/B978-0-12-824349-7.00015-3 – ident: 10.1016/j.eswa.2025.130302_b0145 – volume: 8 start-page: 1427 issue: 12 year: 1997 ident: 10.1016/j.eswa.2025.130302_b0125 article-title: On errors of digital particle image velocimetry publication-title: Measurement Science and Technology doi: 10.1088/0957-0233/8/12/007 – volume: 12 start-page: 8545 issue: 1 year: 2022 ident: 10.1016/j.eswa.2025.130302_b0080 article-title: Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response publication-title: Scientific Reports doi: 10.1038/s41598-022-12364-5 – volume: 24 start-page: 36306 issue: 22 year: 2024 ident: 10.1016/j.eswa.2025.130302_b0010 article-title: Lab-on-Chip Label-Free Sensing System for Electroporation Based on Time-Lapse Microscopy publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2024.3463959 – ident: 10.1016/j.eswa.2025.130302_b0150 – volume: 12 year: 2024 ident: 10.1016/j.eswa.2025.130302_b0165 article-title: PIV-MyoMonitor: An accessible particle image velocimetry-based software tool for advanced contractility assessment of cardiac organoids publication-title: Frontiers in Bioengineering and Biotechnology doi: 10.3389/fbioe.2024.1367141 – volume: 2019 start-page: 1 year: 2019 ident: 10.1016/j.eswa.2025.130302_b0230 article-title: A Comparative Study on Variational Autoencoders and Generative Adversarial Networks publication-title: International Conference of Artificial Intelligence and Information Technology (ICAIIT) – volume: 2022 start-page: 1 year: 2022 ident: 10.1016/j.eswa.2025.130302_b0220 article-title: Anomaly Detection in QAR Data Using VAE-LSTM with Multihead Self-Attention Mechanism publication-title: Mobile Information Systems – year: 2017 ident: 10.1016/j.eswa.2025.130302_b0225 publication-title: Versatile open software to quantify cardiomyocyte and cardiac muscle contraction in vitro and in vivo. – volume: 17 start-page: 3447 issue: 20 year: 2017 ident: 10.1016/j.eswa.2025.130302_b0005 article-title: Skeletal muscle-on-a-chip: An in vitro model to evaluate tissue formation and injury publication-title: Lab on a Chip doi: 10.1039/C7LC00512A – ident: 10.1016/j.eswa.2025.130302_b0315 doi: 10.1016/B978-0-12-336156-1.50061-6 – volume: 258 year: 2024 ident: 10.1016/j.eswa.2025.130302_b0060 article-title: A high-quality self-supervised image denoising method based on SDDW-GAN and CHRNet publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2024.125157 – ident: 10.1016/j.eswa.2025.130302_b0310 doi: 10.1109/TPAMI.2022.3215571 – ident: 10.1016/j.eswa.2025.130302_b0130 – volume: 63 start-page: 139 issue: 11 year: 2020 ident: 10.1016/j.eswa.2025.130302_b0105 article-title: Generative adversarial networks publication-title: Communications of the ACM doi: 10.1145/3422622 – volume: 9 start-page: 12 issue: 1 year: 2021 ident: 10.1016/j.eswa.2025.130302_b0260 article-title: Particle Image Velocimetry for MATLAB: Accuracy and enhanced algorithms in PIVlab publication-title: Journal of Open Research Software doi: 10.5334/jors.334 – volume: 25 issue: 08 year: 2020 ident: 10.1016/j.eswa.2025.130302_b0255 article-title: Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial–mesenchymal transition publication-title: Journal of Biomedical Optics doi: 10.1117/1.JBO.25.8.086502 – start-page: 4322 year: 2020 ident: 10.1016/j.eswa.2025.130302_b0180 article-title: Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model – volume: 2016 start-page: 770 year: 2016 ident: 10.1016/j.eswa.2025.130302_b0120 article-title: Deep Residual Learning for Image Recognition publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 9 start-page: 6789 issue: 1 year: 2019 ident: 10.1016/j.eswa.2025.130302_b0065 article-title: The influence of spatial and temporal resolutions on the analysis of cell-cell interaction: A systematic study for time-lapse microscopy applications publication-title: Scientific Reports doi: 10.1038/s41598-019-42475-5 – volume: 14 issue: 6 year: 2018 ident: 10.1016/j.eswa.2025.130302_b0115 article-title: The impact of temporal sampling resolution on parameter inference for biological transport models publication-title: PLOS Computational Biology doi: 10.1371/journal.pcbi.1006235 – volume: 10 issue: 3 year: 2015 ident: 10.1016/j.eswa.2025.130302_b0305 article-title: From cardiac tissue engineering to heart-on-a-chip: Beating challenges publication-title: Biomedical Materials doi: 10.1088/1748-6041/10/3/034006 – volume: 67 start-page: 1 year: 2013 ident: 10.1016/j.eswa.2025.130302_b0110 article-title: Review of fluid flow and convective heat transfer within rotating disk cavities with impinging jet publication-title: International Journal of Thermal Sciences doi: 10.1016/j.ijthermalsci.2012.11.009 – volume: 72 year: 2021 ident: 10.1016/j.eswa.2025.130302_b0045 article-title: Recursive Deep Prior Video: A super resolution algorithm for time-lapse microscopy of organ-on-chip experiments publication-title: Medical Image Analysis doi: 10.1016/j.media.2021.102124 – volume: 128 start-page: 775 issue: 6 year: 2021 ident: 10.1016/j.eswa.2025.130302_b0040 article-title: Cardiac Tissues From Stem Cells: New Routes to Maturation and Cardiac Regeneration publication-title: Circulation Research doi: 10.1161/CIRCRESAHA.121.318183 – volume: 32 start-page: 99 issue: 1 year: 2013 ident: 10.1016/j.eswa.2025.130302_b0200 article-title: Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2012.2220375 – volume: 72 start-page: 1 year: 2023 ident: 10.1016/j.eswa.2025.130302_b0185 article-title: Uncertainty-Based Feature Selection for Improved Adequacy of Dermoscopic Image Classification publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2023.3303498 – volume: 38 start-page: 517 year: 2014 ident: 10.1016/j.eswa.2025.130302_b0015 article-title: Investigation of cell dynamics in vitro by time lapse microscopy and image analysis publication-title: Chemical Engineering Transactions – volume: 232 year: 2023 ident: 10.1016/j.eswa.2025.130302_b0050 article-title: S3-VAE: A novel Supervised-Source-Separation Variational AutoEncoder algorithm to discriminate tumor cell lines in time-lapse microscopy images publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.120861 – volume: 131 start-page: 251 year: 2020 ident: 10.1016/j.eswa.2025.130302_b0270 article-title: Deep learning on image denoising: An overview publication-title: Neural Networks doi: 10.1016/j.neunet.2020.07.025 – volume: 2 year: 2014 ident: 10.1016/j.eswa.2025.130302_b0265 article-title: PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB publication-title: Journal of Open Research Software doi: 10.5334/jors.bl – year: 1996 ident: 10.1016/j.eswa.2025.130302_b0210 – volume: 4 start-page: 185S issue: 2_Pt_2 year: 1991 ident: 10.1016/j.eswa.2025.130302_b0240 article-title: Negative Inotropic Activity of the Calcium Antagonists Isradipine, Nifedipine, Diltiazem, and Verapamil in Diseased Human Myocardium publication-title: American Journal of Hypertension doi: 10.1093/ajh/4.2.185S – volume: 22 start-page: 400 issue: 3 year: 1951 ident: 10.1016/j.eswa.2025.130302_b0215 article-title: A Stochastic Approximation Method publication-title: The Annals of Mathematical Statistics doi: 10.1214/aoms/1177729586 – volume: 35 start-page: 2291 issue: 3 year: 2023 ident: 10.1016/j.eswa.2025.130302_b0055 article-title: A survey on deep learning applied to medical images: From simple artificial neural networks to generative models publication-title: Neural Computing & Applications doi: 10.1007/s00521-022-07953-4 – volume: 82 start-page: 1338 issue: 3 year: 2002 ident: 10.1016/j.eswa.2025.130302_b0275 article-title: Sustained Overexpression of IGF-1 Prevents Age-Dependent Decrease in Charge Movement and Intracellular Ca2+ in Mouse Skeletal Muscle publication-title: Biophysical Journal doi: 10.1016/S0006-3495(02)75489-1 – volume: 33 start-page: 3671 issue: 8 year: 2021 ident: 10.1016/j.eswa.2025.130302_b0070 article-title: Multi-scale generative adversarial network for improved evaluation of cell–cell interactions observed in organ-on-chip experiments publication-title: Neural Computing and Applications doi: 10.1007/s00521-020-05226-6 – volume: 6 start-page: 241 issue: 1 year: 2023 ident: 10.1016/j.eswa.2025.130302_b0190 article-title: Deep-Manager: A versatile tool for optimal feature selection in live-cell imaging analysis publication-title: Communications Biology doi: 10.1038/s42003-023-04585-9 – start-page: 1 year: 2018 ident: 10.1016/j.eswa.2025.130302_b0300 article-title: On Definition of Deep Learning – volume: 10 start-page: 15635 issue: 1 year: 2020 ident: 10.1016/j.eswa.2025.130302_b0075 article-title: Accelerating the experimental responses on cell behaviors: A long-term prediction of cell trajectories using Social Generative Adversarial Network publication-title: Scientific Reports doi: 10.1038/s41598-020-72605-3 – volume: 75 start-page: 704 issue: 2 year: 2019 ident: 10.1016/j.eswa.2025.130302_b0135 article-title: Medical image denoising using convolutional neural network: A residual learning approach publication-title: The Journal of Supercomputing doi: 10.1007/s11227-017-2080-0 – volume: 4 start-page: 636 issue: 7 year: 2022 ident: 10.1016/j.eswa.2025.130302_b0245 article-title: Learning biophysical determinants of cell fate with deep neural networks publication-title: Nature Machine Intelligence doi: 10.1038/s42256-022-00503-6 – volume: 16 issue: 3 year: 2024 ident: 10.1016/j.eswa.2025.130302_b0175 article-title: Pillar electrodes embedded in the skeletal muscle tissue for selective stimulation of biohybrid actuators with increased contractile distance publication-title: Biofabrication doi: 10.1088/1758-5090/ad4ba1 – volume: 238 year: 2024 ident: 10.1016/j.eswa.2025.130302_b0290 article-title: Kernel adaptive memory network for blind video super-resolution publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.122252 – volume: 5 start-page: 238 year: 2024 ident: 10.1016/j.eswa.2025.130302_b0140 article-title: BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs publication-title: IEEE Open Journal of Engineering in Medicine and Biology doi: 10.1109/OJEMB.2024.3377461 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.eswa.2025.130302_b0160 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 22 start-page: 79 issue: 1 year: 1951 ident: 10.1016/j.eswa.2025.130302_b0155 article-title: On Information and Sufficiency publication-title: The Annals of Mathematical Statistics doi: 10.1214/aoms/1177729694 – volume: 2023 start-page: 71 year: 2024 ident: 10.1016/j.eswa.2025.130302_b0095 article-title: Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells publication-title: Eurosensors – volume: 1 issue: 4 year: 2021 ident: 10.1016/j.eswa.2025.130302_b0235 article-title: Dense optical flow software to quantify cellular contractility publication-title: Cell Reports Methods doi: 10.1016/j.crmeth.2021.100044 – volume: 81 start-page: 197 issue: 1 year: 2024 ident: 10.1016/j.eswa.2025.130302_b0250 article-title: Engineered platforms for mimicking cardiac development and drug screening publication-title: Cellular and Molecular Life Sciences doi: 10.1007/s00018-024-05231-1 – volume: 96 year: 2024 ident: 10.1016/j.eswa.2025.130302_b0025 article-title: Video-based heart rate estimation from challenging scenarios using synthetic video generation publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2024.106598 – volume: 21 start-page: 11374 issue: 10 year: 2021 ident: 10.1016/j.eswa.2025.130302_b0205 article-title: A New Approach to Polyp Detection by Pre-Processing of Images and Enhanced Faster R-CNN publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2020.3036005 – ident: 10.1016/j.eswa.2025.130302_b0090 doi: 10.1007/3-540-45103-X_50 – ident: 10.1016/j.eswa.2025.130302_b0285 doi: 10.1190/IGC2018-113 – year: 2017 ident: 10.1016/j.eswa.2025.130302_b0100 article-title: Three-dimensional cardiac microtissues composed of cardiomyocytes and endothelial cells co-differentiated from human pluripotent stem cells publication-title: Development doi: 10.1242/dev.143438 – volume: 415 start-page: 198 issue: 6868 year: 2002 ident: 10.1016/j.eswa.2025.130302_b0030 article-title: Cardiac excitation–contraction coupling publication-title: Nature doi: 10.1038/415198a – volume: 70 start-page: 2237 issue: 7 year: 2023 ident: 10.1016/j.eswa.2025.130302_b0170 article-title: Dynamic Control of Contractile Force in Engineered Heart Tissue publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2023.3239594 |
| SSID | ssj0017007 |
| Score | 2.4852285 |
| Snippet | Deep learning has proven to be one of the most effective methods in analyzing biological images to extract parameters fundamental for studying physiological... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 130302 |
| SubjectTerms | Contraction analysis Data restoration Time-lapse microscopy Variational autoencoders |
| Title | VAE-MOTION: A deep generative model for cardiomyocyte contractility analysis for improving drug efficacy evaluation |
| URI | https://dx.doi.org/10.1016/j.eswa.2025.130302 |
| Volume | 299 |
| WOSCitedRecordID | wos001615058200007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect Freedom Collection - Elsevier issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0017007 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3JbtswECVcp4deuhdJN_DQW6FAC0lJvRmFuyFNAzQtfBMochQ4cGVDltP4V_q15SrJ6YLm0ItgEPRI0DyQo-GbNwi9gETr_vEkEIzygECSB3kW0oCVEQshDSNeGXX9o_T4OJvN8pPR6IevhblYpHWdXV7mq__qajWmnK1LZ6_h7s6oGlC_ldPVVbldXf_J8V8n0-DjJ515slXnEmClGyWD0_g2vW8Mu1AYLuq37VJsW0da12UOJjDnXqxET5x3mQfZbM40B0S5VmwHUuE7CX6tntw6jWhfPTc4J--OPTaNMFyCt_NlczbvKUN8bTkGJ3y56EY_84WJc482YpBDALWDc1dzpM8g-DCLEbOextWlI9OARLZjj1-ZY9s7ya2terc11dm_Lvs2A3F-COvvWksqpof95F2N7St7X8dI9GS380LbKLSNwtq4gfbilObZGO1N3k9nH7ozqjS0xfj-yV1JlmUPXn2S34c9g1Dm9C667b5B8MRi5x4aQX0f3fH9PbBb7h-gdQ-lV3iCNZBwDyRsgIQVPvAOkPAOkLAHkpnYAQlrIGEPJNwD6SH68mZ6-vpd4Jp0BCKmSRuIhGeECCCUsKwKRShlSSVJZFZJyCmEErI4UhsJhRwYFWlMOcnjSoXdeZRynjxC43pZwz7CVHeHpLJkDCIimbJQhrKSjJOsiqAMD9BL_w6LldViKf7stwNE_WsuXDRpo8RCoeYv_3t8rbs8Qbd6OD9F47bZwDN0U1y083Xz3EHmJ3Ygmbg |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=VAE-MOTION%3A+A+deep+generative+model+for+cardiomyocyte+contractility+analysis+for+improving+drug+efficacy+evaluation&rft.jtitle=Expert+systems+with+applications&rft.au=Curci%2C+Giorgia&rft.au=Casti%2C+Paola&rft.au=Sala%2C+Luca&rft.au=Brescia%2C+Marcella&rft.date=2026-03-01&rft.issn=0957-4174&rft.volume=299&rft.spage=130302&rft_id=info:doi/10.1016%2Fj.eswa.2025.130302&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_eswa_2025_130302 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |