{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T18:25:17Z","timestamp":1787250317298,"version":"build-2736575974"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013441","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000}}],"reference-count":66,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Engineering Research Council","award":["RGPIN\/05117-2014"],"award-info":[{"award-number":["RGPIN\/05117-2014"]}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["PJT-155980"],"award-info":[{"award-number":["PJT-155980"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Canadian Foundation for Innovation","award":["32581"],"award-info":[{"award-number":["32581"]}]},{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs Program","doi-asserted-by":"crossref","award":["950-231541"],"award-info":[{"award-number":["950-231541"]}],"id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003151","name":"Fonds de Recherche du Qu\u00e9bec \u2013 Nature et Technologies","doi-asserted-by":"publisher","award":["300853"],"award-info":[{"award-number":["300853"]}],"id":[{"id":"10.13039\/501100003151","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Quantitative phenotyping of\n                    <jats:italic>Caenorhabditis elegans<\/jats:italic>\n                    is essential across numerous fields, yet data extraction remains a significant analytical bottleneck. Traditional segmentation methods based on pixel-intensity thresholding are highly sensitive to variations in imaging conditions and often fail in the presence of noise, overlaps, or uneven illumination. These failures necessitate meticulous experimental setups, expensive hardware, or extensive manual curation, which reduces throughput and introduces bias. Here, we introduce TWARDIS (Tools for Worm Automated Recognition &amp; Dynamic Imaging System), a modular, Python-based analysis suite that leverages large foundation vision models, specifically the Segment Anything Models (SAM and SAM2) and a fine-tuned vision transformer classifier, to overcome some of these limitations. We demonstrate the versatility of an AI compound system approach across diverse modalities. For static morphological analysis, TWARDIS successfully resolved overlapping worms in noisy images without human intervention, showing a 0.999 correlation with manual segmentation. In behavioral assays (swimming and crawling), the pipeline enabled high-definition postural analysis even in low-resolution, wide-field recordings where the worm occupied only ~0.25% of the field of view, accurately resolving complex postures without frame rejection. Finally, when applied to calcium imaging of semi-restricted animals, TWARDIS provided precise, frame-by-frame segmentation of neural compartments, reducing the artificial signal flattening common in traditional region-of-interest-based approaches and enabling the extraction of biologically accurate, absolute head positions. The system\u2019s hardware-scalable architecture and modular design ensure both current accessibility and future improvements without restructuring. By automating some of the most time-consuming aspects of image analysis, TWARDIS removes many critical bottlenecks and tradeoffs in\n                    <jats:italic>C. elegans<\/jats:italic>\n                    research, enabling researchers to focus on biological questions rather than technical image processing challenges.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1013441","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T17:47:06Z","timestamp":1784742426000},"page":"e1013441","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large vision model framework for automated C. elegans analysis: From static morphometry to dynamic neural activity"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1102-6017","authenticated-orcid":true,"given":"Aur\u00e9lie","family":"Guisnet","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3408-3858","authenticated-orcid":true,"given":"Michael","family":"Hendricks","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"pcbi.1013441.ref001","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/1471-2156-6-5","article-title":"An automated system for measuring parameters of nematode sinusoidal movement","volume":"6","author":"CJ Cronin","year":"2005","journal-title":"BMC Genet"},{"key":"pcbi.1013441.ref002","article-title":"findWormz is a user-friendly automated fluorescence quantification method for C. elegans research","volume":"2025","author":"E Kitto","year":"2025","journal-title":"MicroPubl Biol"},{"issue":"1","key":"pcbi.1013441.ref003","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s12915-017-0477-0","article-title":"WorMachine: machine learning-based phenotypic analysis tool for worms","volume":"16","author":"A Hakim","year":"2018","journal-title":"BMC Biol"},{"issue":"2","key":"pcbi.1013441.ref004","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0057142","article-title":"WormSizer: high-throughput analysis of nematode size and shape","volume":"8","author":"BT Moore","year":"2013","journal-title":"PLoS One"},{"key":"pcbi.1013441.ref005","article-title":"Automated behavioral analysis of large C. elegans populations using a wide field-of-view tracking platform","author":"M Perni","year":"2018","journal-title":"J Vis Exp"},{"key":"pcbi.1013441.ref006","doi-asserted-by":"crossref","first-page":"100795","DOI":"10.1016\/j.softx.2021.100795","article-title":"AniLength: GUI-based automatic worm length measurement software using image processing and deep neural network","volume":"15","author":"S-K Jung","year":"2021","journal-title":"SoftwareX"},{"issue":"7","key":"pcbi.1013441.ref007","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1003702","article-title":"CeleST: computer vision software for quantitative analysis of C. elegans swim behavior reveals novel features of locomotion","volume":"10","author":"C Restif","year":"2014","journal-title":"PLoS Comput Biol"},{"issue":"4","key":"pcbi.1013441.ref008","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1009942","article-title":"Deep learning for robust and flexible tracking in behavioral studies for C. elegans","volume":"18","author":"K Bates","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1013441.ref009","author":"L Wang","year":"2020"},{"issue":"17","key":"pcbi.1013441.ref010","doi-asserted-by":"crossref","first-page":"9675","DOI":"10.3390\/ijms25179675","article-title":"WormCNN-Assisted Establishment and Analysis of Glycation Stress Models in C. elegans: Insights into Disease and Healthy Aging","volume":"25","author":"Y Pan","year":"2024","journal-title":"Int J Mol Sci"},{"key":"pcbi.1013441.ref011","author":"WH Weheliye","year":"2024"},{"issue":"1","key":"pcbi.1013441.ref012","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1038\/s42003-023-05098-1","article-title":"Fast detection of slender bodies in high density microscopy data","volume":"6","author":"A Alonso","year":"2023","journal-title":"Commun Biol"},{"issue":"1","key":"pcbi.1013441.ref013","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1038\/s42003-022-03206-1","article-title":"Megapixel camera arrays enable high-resolution animal tracking in multiwell plates","volume":"5","author":"IL Barlow","year":"2022","journal-title":"Commun Biol"},{"key":"pcbi.1013441.ref014","first-page":"113","volume-title":"Deep learning in automated worm identification and tracking for C. elegan mating behaviour analysis. Lecture Notes in Computer Science","author":"CH Akpu","year":"2025"},{"key":"pcbi.1013441.ref015","doi-asserted-by":"crossref","first-page":"113459","DOI":"10.1016\/j.asoc.2025.113459","article-title":"A bilayer segmentation-recombination network for accurate segmentation of overlapping C. elegans","volume":"180","author":"M Ding","year":"2025","journal-title":"Appl Soft Comput"},{"key":"pcbi.1013441.ref016","doi-asserted-by":"crossref","DOI":"10.1101\/2020.07.09.193755","article-title":"WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans","author":"L Hebert","year":"2020"},{"issue":"1","key":"pcbi.1013441.ref017","doi-asserted-by":"crossref","first-page":"8870","DOI":"10.1038\/s41598-025-93533-0","article-title":"A high precision method of segmenting complex postures in Caenorhabditis elegans and deep phenotyping to analyze lifespan","volume":"15","author":"B Dong","year":"2025","journal-title":"Sci Rep"},{"key":"pcbi.1013441.ref018","author":"SC Banerjee","year":"2022"},{"key":"pcbi.1013441.ref019","author":"A Kirillov","year":"2023"},{"key":"pcbi.1013441.ref020","author":"N Ravi","year":"2024"},{"issue":"3","key":"pcbi.1013441.ref021","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1038\/s41592-024-02580-4","article-title":"Segment Anything for Microscopy","volume":"22","author":"A Archit","year":"2025","journal-title":"Nat Methods"},{"key":"pcbi.1013441.ref022","author":"J Zhu","year":"2024"},{"key":"pcbi.1013441.ref023","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"J Ma","year":"2024","journal-title":"Nat Commun"},{"issue":"9","key":"pcbi.1013441.ref024","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0139724","article-title":"WormGender - Open-Source Software for Automatic Caenorhabditis elegans Sex Ratio Measurement","volume":"10","author":"MK Labocha","year":"2015","journal-title":"PLoS One"},{"key":"pcbi.1013441.ref025","author":"E Bonnard","year":"2022"},{"key":"pcbi.1013441.ref026","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/1471-2202-10-84","article-title":"Fast, automated measurement of nematode swimming (thrashing) without morphometry","volume":"10","author":"SD Buckingham","year":"2009","journal-title":"BMC Neurosci"},{"key":"pcbi.1013441.ref027","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.3390\/app13042274","article-title":"AniWellTracker: Image analysis of small animal locomotion in multiwell plates","volume":"13","author":"SK Jung","year":"2023","journal-title":"Appl Sci"},{"key":"pcbi.1013441.ref028","author":"K Vedantham","year":"2024"},{"issue":"3","key":"pcbi.1013441.ref029","doi-asserted-by":"crossref","first-page":"5377","DOI":"10.1007\/s11357-025-01631-8","article-title":"WormRACER: Robust Analysis by Computer-Enhanced Recording","volume":"47","author":"BT Van Camp","year":"2025","journal-title":"Geroscience"},{"key":"pcbi.1013441.ref030","author":"EI Yemini","year":"2014"},{"key":"pcbi.1013441.ref031","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.jneumeth.2018.02.005","article-title":"Massively parallel C. elegans tracking provides multi-dimensional fingerprints for phenotypic discovery","volume":"306","author":"M Perni","year":"2018","journal-title":"J Neurosci Methods"},{"issue":"6","key":"pcbi.1013441.ref032","article-title":"A Gate-and-Switch Model for Head Orientation Behaviors in Caenorhabditis elegans","volume":"5","author":"M-H Ouellette","year":"2018","journal-title":"eNeuro"},{"issue":"7","key":"pcbi.1013441.ref033","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1038\/nmeth.2019","article-title":"Fiji: an open-source platform for biological-image analysis","volume":"9","author":"J Schindelin","year":"2012","journal-title":"Nat Methods"},{"key":"pcbi.1013441.ref034","doi-asserted-by":"crossref","DOI":"10.1002\/cpns.17","article-title":"The Movement Tracker: A Flexible System for Automated Movement Analysis in Invertebrate Model Organisms","volume":"77","author":"L Mouchiroud","year":"2016","journal-title":"Curr Protoc Neurosci"},{"issue":"9","key":"pcbi.1013441.ref035","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1010415","article-title":"A hierarchical process model links behavioral aging and lifespan in C. elegans","volume":"18","author":"N Oswal","year":"2022","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"pcbi.1013441.ref036","article-title":"QuantWorm: a comprehensive software package for Caenorhabditis elegans phenotypic assays","volume":"9","author":"S-K Jung","year":"2014","journal-title":"PLoS One"},{"issue":"9","key":"pcbi.1013441.ref037","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1038\/nmeth.2560","article-title":"A database of Caenorhabditis elegans behavioral phenotypes","volume":"10","author":"E Yemini","year":"2013","journal-title":"Nat Methods"},{"issue":"2","key":"pcbi.1013441.ref038","doi-asserted-by":"crossref","DOI":"10.1242\/bio.062282","article-title":"The impact of rearing environment on C. elegans: phenotypic, transcriptomic and intergenerational responses to 3D enriched habitats","volume":"15","author":"A Guisnet","year":"2026","journal-title":"Biology Open"},{"key":"pcbi.1013441.ref039","article-title":"A novel epifluorescence microscope design and software package to record naturalistic behaviour and cell activity in freely moving Caenorhabditis elegans","author":"M Hendricks","year":"2025","journal-title":"bioRxiv"},{"key":"pcbi.1013441.ref040","author":"J Ao","year":"2024"},{"key":"pcbi.1013441.ref041","author":"J Cai","year":"2022"},{"key":"pcbi.1013441.ref042","author":"T Lee","year":"2025"},{"key":"pcbi.1013441.ref043","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.2574","article-title":"Review of models for estimating 3D human pose using deep learning","volume":"11","author":"S Salisu","year":"2025","journal-title":"PeerJ Comput Sci"},{"key":"pcbi.1013441.ref044","author":"SS Bhattacharjee","year":"2024"},{"issue":"11","key":"pcbi.1013441.ref045","doi-asserted-by":"crossref","DOI":"10.1002\/cpz1.288","article-title":"Three-Dimensional Fruit Tissue Habitats for Culturing Caenorhabditis elegans","volume":"1","author":"A Guisnet","year":"2021","journal-title":"Curr Protoc"},{"key":"pcbi.1013441.ref046","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1091\/mbc.E20-11-0716","article-title":"CentTracker: a trainable, machine-learning-based tool for large-scale analyses of Caenorhabditis elegans germline stem cell mitosis","volume":"32","author":"RM Zellag","year":"2021","journal-title":"Mol Biol Cell"},{"key":"pcbi.1013441.ref047","doi-asserted-by":"crossref","unstructured":"Ansel J, Yang E, He H, Gimelshein N, Jain A, Voznesensky M, et al. PyTorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2. New York (NY): ACM; 2024. pp. 929\u2013947.","DOI":"10.1145\/3620665.3640366"},{"key":"pcbi.1013441.ref048"},{"key":"pcbi.1013441.ref049","author":"A Dosovitskiy","year":"2020"},{"key":"pcbi.1013441.ref050","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2009. p. 248\u2013255.","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"1758","key":"pcbi.1013441.ref051","doi-asserted-by":"crossref","first-page":"20170375","DOI":"10.1098\/rstb.2017.0375","article-title":"Powerful and interpretable behavioural features for quantitative phenotyping of Caenorhabditis elegans","volume":"373","author":"A Javer","year":"2018","journal-title":"Philosophical Transactions of the Royal Society B: Biological Sciences"},{"issue":"1","key":"pcbi.1013441.ref052","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1093\/genetics\/77.1.71","article-title":"The genetics of Caenorhabditis elegans","volume":"77","author":"S Brenner","year":"1974","journal-title":"Genetics"},{"issue":"7405","key":"pcbi.1013441.ref053","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1038\/nature11081","article-title":"Compartmentalized calcium dynamics in a C. elegans interneuron encode head movement","volume":"487","author":"M Hendricks","year":"2012","journal-title":"Nature"},{"key":"pcbi.1013441.ref054","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1895\/wormbook.1.162.1","article-title":"Microfluidics as a tool for C. elegans research","author":"A San-Miguel","year":"2013","journal-title":"WormBook"},{"key":"pcbi.1013441.ref055","doi-asserted-by":"crossref","first-page":"56","DOI":"10.25080\/Majora-92bf1922-00a","article-title":"Data Structures for Statistical Computing in Python.","volume-title":"Proceedings of the 9th Python in Science Conference","author":"W. McKinney","year":"2010"},{"issue":"3","key":"pcbi.1013441.ref056","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"JD Hunter","year":"2007","journal-title":"Comput Sci Eng"},{"issue":"7825","key":"pcbi.1013441.ref057","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"CR Harris","year":"2020","journal-title":"Nature"},{"key":"pcbi.1013441.ref058"},{"key":"pcbi.1013441.ref059","article-title":"The OpenCV Library","author":"G Bradski","year":"2000","journal-title":"Dr Dobb\u2019s Journal of Software Tools"},{"key":"pcbi.1013441.ref060","article-title":"tqdm: A fast, Extensible Progress Bar for Python and CLI","author":"da Costa-Luis C, Larroque SK, Altendorf K, Mary H, richardsheridan, Korobov M, et al","year":"2024","journal-title":"Zenodo"},{"issue":"60","key":"pcbi.1013441.ref061","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"seaborn: statistical data visualization","volume":"6","author":"M Waskom","year":"2021","journal-title":"JOSS"},{"issue":"3","key":"pcbi.1013441.ref062","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific computing in Python","volume":"17","author":"P Virtanen","year":"2020","journal-title":"Nat Methods"},{"key":"pcbi.1013441.ref063","author":"The python language reference"},{"key":"pcbi.1013441.ref064","year":"2025"},{"key":"pcbi.1013441.ref065","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.453","article-title":"scikit-image: image processing in Python","volume":"2","author":"S van der Walt","year":"2014","journal-title":"PeerJ"},{"key":"pcbi.1013441.ref066","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"F Pedregosa","year":"2011","journal-title":"J Mach Learning Res"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1013441","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013441","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T17:45:54Z","timestamp":1787247954000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013441"}},"subtitle":[],"editor":[{"given":"Jian","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,22]]},"references-count":66,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,22]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1013441","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,22]]}}}