[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125820-en":3,"doc-seo-125820-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},125820,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding","Segmentation of computed tomography (CT) is critical for clinical cardiac procedures, especially personalized ablation for atrial fibrillation, but it is limited by the need for large labeled datasets. This work combines machine learning with explicit domain knowledge by encoding cardiac geometry. A virtual-dissection model represents atrial anatomy with geometric shapes, enabling ML training on a small set of digital hearts and evaluation in independent cohorts and a prospective atrial fibrillation ablation study.","TYPE Original Research PUBLISHED 02 October 2023 DOI 10.3389/fcvm.2023.1189293  \nEDITED BY  \nAlfredo Vellido,  \nUniversitat Politecnica de Catalunya, Spain  \nREVIEWED BY  \nAngela Lungu,  \nTechnical University of Cluj-Napoca, Romania Elena Tolkacheva,  \nUniversity of Minnesota Twin Cities, United States  \n*CORRESPONDENCE  \nSanjiv M. Narayan  \n [sanjiv1@stanford.edu](sanjiv1@stanford.edu)  \nRECEIVED 18 March 2023  \nACCEPTED 18 September 2023  \nPUBLISHED 02 October 2023  \nCITATION  \nFeng R, Deb B, Ganesan P, Tjong FVY, Rogers AJ, Ruipérez-Campillo S, Somani S, Clopton P, Baykaner T, Rodrigo M, Zou J, Haddad F, Zahari M and Narayan SM (2023) Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding.  \nFront. Cardiovasc. Med. 10:1189293 .  \ndoi: 10.3389/fcvm.2023.1189293  \nCOPYRIGHT  \n© 2023 Feng, Deb, Ganesan, Tjong, Rogers, Ruipérez-Campillo, Somani, Clopton, Baykaner, Rodrigo, Zou, Haddad, Zahari and Narayan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nSegmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding  \nRuibin Feng1, Brototo Deb1, Prasanth Ganesan1, Fleur V. Y. Tjong1,2, Albert J. Rogers1, Samuel Ruipérez-Campillo1,3, Sulaiman Somani1, Paul Clopton1, Tina Baykaner1, Miguel Rodrigo1,4, James Zou5, Francois Haddad1, Matei Zahari6 and Sanjiv M. Narayan1*  \n1Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, United States, 2Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands, 3Bioengineering Department, University of California, Berkeley, Berkeley, CA, United States, 4CoMMLab, Universitat Politècnica de València, Valencia, Spain, 5Department of Biomedical Data Science, Stanford University, Stanford, CA, United States, 6Department of Computer Science, Stanford University, Stanford, CA, United States  \nBackground: Segmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation.  \nMethods: We mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of N =6 digital hearts. The model, termed “virtual dissection,” was used to train ML to segment cardiac CT in N = 20 patients, then tested in independent datasets and in a prospective study.  \nResults: In independent test cohorts (N = 160) from 2 Institutions with different CT scanners, atrial structures were accurately segmented with Dice scores of 96.7% in internal (IQR: 95.3%–97.7%) and 93.5% in external (IQR: 91.9%–94.7%) test data, with good agreement with experts (r = 0 . 99; p \u003C 0 . 0001) . In a prospective study of 42 patients at ablation, this approach reduced segmentation time by 85%(2 .3 ± 0 . 8 vs. 15. 0 ± 6 . 9 min, p \u003C 0 . 0001), yet provided similar Dice scores to experts (93 . 9%(IQR: 93 . 0%–94 . 6%) vs. 94.4%(IQR: 92 . 8%–95 .7%), p = NS) . Conclusions: Encoding cardiac geometry using mathematical models greatly accelerated training of ML to segment CT, reducing the need for","cbCailyH6AvMerxN","https://ap.wps.com/l/cbCailyH6AvMerxN","pdf",14713469,1,11,"English","en",105,"# Introduction\n## Clinical importance and challenges of cardiac CT segmentation\n## Limitations of large labeled datasets in ML\n# Methods\n## Virtual dissection and geometric modeling of atrial anatomy\n## Training, independent testing, and prospective evaluation\n# Results\n## Segmentation accuracy and expert agreement\n## Prospective ablation impact on segmentation time\n# Conclusions","[{\"question\":\"What problem does the study address in cardiac CT segmentation?\",\"answer\":\"Accurate CT segmentation for ablation is time-consuming and depends on large amounts of labeled training data, which are difficult to obtain in medical settings.\"},{\"question\":\"How is domain knowledge incorporated into the machine learning approach?\",\"answer\":\"The method encodes cardiac geometry using mathematically defined representations of atrial anatomy, enabling a “virtual dissection” model to guide segmentation training with fewer labeled examples.\"},{\"question\":\"What are the reported performance and workflow effects?\",\"answer\":\"In independent test cohorts, atrial structures achieved high Dice scores and strong agreement with experts. In a prospective ablation study, segmentation time was reduced substantially while maintaining similar accuracy to expert annotations.\"}]","Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding | PDF",1785901394,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"segmenting-computed-tomograms-for-cardiac-ablation-using-machine-learning-leveraged-by-domain-knowledge-encoding","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/segmenting-computed-tomograms-for-cardiac-ablation-using-machine-learning-leveraged-by-domain-knowledge-encoding/125820/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in cardiac CT segmentation?","Question",{"text":75,"@type":76},"Accurate CT segmentation for ablation is time-consuming and depends on large amounts of labeled training data, which are difficult to obtain in medical settings.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is domain knowledge incorporated into the machine learning approach?",{"text":80,"@type":76},"The method encodes cardiac geometry using mathematically defined representations of atrial anatomy, enabling a “virtual dissection” model to guide segmentation training with fewer labeled examples.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the reported performance and workflow effects?",{"text":84,"@type":76},"In independent test cohorts, atrial structures achieved high Dice scores and strong agreement with experts. 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