[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119943-en":3,"doc-seo-119943-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119943,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",6,"Technology","New interactive machine learning tool for marine image analysis","Advancing imaging technologies accelerate marine video and image data collection, yet extracting species-level information remains highly time-consuming and limits dataset value. This study demonstrates RootPainter, an open-source interactive machine learning tool, for fast and accurate analysis of large marine image datasets. RootPainter was used to detect and measure cold-water coral reef associated sponge Mycale lingua across two datasets, integrating corrective annotation metrics to objectively determine training stopping points and reduce manual validation. High-accuracy models achieved an average Dice score of 0.94 ± 0.06.","Scotland's Rural College  \nNew interactive machine learning tool for marine image analysis  \nClark, H. Poppy; Smith, Abraham George; McKay Fletcher, Daniel; Larsson, Ann I. ; Jaspars, Marcel; De Clippele, Laurence H.  \nPublished in:  \nRoyal Society Open Science  \nDOI:  \n10.1098/rsos.231678  \nFirst published: 29/05/2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication  \nCitation for pulished version (APA):  \nClark, H. P. , Smith, A. G. , McKay Fletcher, D. , Larsson, A. I. , Jaspars, M. , & De Clippele, L. H. (2024) . New interactive machine learning tool for marine image analysis. Royal Society Open Science, 11(5) . Advance online publication. [https://doi.org/10.1098/rsos.231678](https://doi.org/10.1098/rsos.231678)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal ?  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 09. Jun. 2024  \nCite this article: Clark HP, Smith AG, McKay Fletcher D, Larsson AI, Jaspars M, De Clippele LH. 2024 New interactive machine learning tool for marine image analysis. R. Soc. Open Sci. 11: 231678.  \n[https://doi.org/10.1098/rsos.231678](https://doi.org/10.1098/rsos.231678)  \n[Received: 3 November 2023](Received: 3 November 2023)  \n[Accepted: 2 April 2024](Accepted: 2 April 2024)  \nSubject Category:  \nEcology, conservation, and global change biology  \nSubject Areas:  \necology, computer vision, marine biology  \nKeywords:  \nmarine image analysis, interactive machine learning, automated area measurement, RootPainter, benthic ecology, computer vision  \nAuthor for correspondence:  \nH. Poppy Clark  \ne-mail: [h.clark.21@abdn.ac.uk](h.clark.21@abdn.ac.uk)  \nElectronic supplementary material is available online at [https://doi.org/10.6084/](https://doi.org/10.6084/)[ ](https://doi.org/10.6084/)[m9.figshare.c.7184015.](m9.figshare.c.7184015.)  \nNew interactive machine learning tool for marine image analysis  \nH. Poppy Clark1, Abraham George Smith2, Daniel McKay Fletcher3, Ann I. Larsson4, Marcel Jaspars1 and Laurence  \nH. De Clippele5  \n1Marine Biodiscovery Centre, Department of Chemistry, University of Aberdeen, Aberdeen AB24 3UE, UK  \n2Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark 3Rural Economy, Environment and Society, Scotland’s Rural College, Edinburgh EH9 3JG, UK 4Tjärnö Marine Laboratory, Department of Marine Sciences, University of Gothenburg, Sweden 5School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow G61 1QH, UK  \n HPC, 0000-0002-1667-9922; AGS, 0000-0001-9782-2825; DMKF, 0000-0001-6569-2931; MJ, 0000-0002-2426-6028; LHDC, 0000-0002-4097-274X  \nAdvancing imaging technologies are drastically increasing the rate of marine video and image data collection. Often these datasets are not analysed to their full potential as extracting information for multiple species is incredibly time-consuming. This study demonstrates the capability of the open-source interactive machine learning tool, RootPainter, to analyse large marine image datasets quickly and accurately. The ability of RootPainter to extract the presence and surface area of the cold-water coral reef associate sponge species, Mycale lingua, was tested in two datasets: 18 346 time-lapse images and 1420 remotely operated ve","cbCaidvy6i8Ergr1","https://ap.wps.com/l/cbCaidvy6i8Ergr1","pdf",3746391,1,24,"English","en",105,"# Introduction\n## Problem of time-consuming marine image annotation\n## Development trend toward machine learning for marine ecology","[{\"question\":\"What problem does the study address in marine image analysis?\",\"answer\":\"Marine video and image datasets are collected faster than they can be analyzed, and extracting species presence or size is often prohibitively time-consuming.\"},{\"question\":\"Which tool is evaluated for interactive machine learning in this research?\",\"answer\":\"The study evaluates RootPainter, an open-source interactive machine learning tool for analyzing large marine image datasets quickly and accurately.\"},{\"question\":\"How did the approach improve model training and annotation efficiency?\",\"answer\":\"New corrective annotation metrics help determine when to stop training, reducing the need for manual validation, and transfer learning improved analysis efficiency substantially versus manual annotation.\"}]","New interactive machine learning tool for marine image analysis | 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