[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126965-en":3,"doc-seo-126965-105":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},126965,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery","Objectives: Develop a machine learning workflow to analyze postoperative corneal endothelial cell images from patients undergoing Descemet’s membrane keratoplasty (DMEK). Methods: Propose and validate an AlexNet-based approach for endothelial cell segmentation and cell localization, trained with an R-CNN for cell positioning. Analyze 506 postoperative images to segment cells, derive polygonal structure, and quantify density and hexagonality by identifying ridges between adjacent cells. Results: Achieve 86.15% accuracy and F1 0.857, with AUC 0.764, supporting reliable replacement of manual detection in in vivo confocal microscopy. Conclusions: Segmentation-focused models can assess endothelium health in DMEK patients.","University of Groningen  \nMachine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery  \nKaraca, Emine Esra; Işlk, Feyza Dicle; Hassanpour, Reza; Oztoprak, Kaslm; Evren Kemer,Özlem  \nPublished in:  \nBiomedizinische Technik  \nDOI:  \n10.1515/bmt-2023-0126  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2024  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nKaraca, E. E. , Işlk, F. D. , Hassanpour, R. , Oztoprak, K. , & Evren Kemer, Ö . (2024) . Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery. Biomedizinische Technik, 69(5), 481-489 . [https://doi.org/10.1515/bmt-2023-0126](https://doi.org/10.1515/bmt-2023-0126)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \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.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 01-01-2026  \nEmine Esra Karaca, Feyza Dicle Işık, Reza Hassanpour, Kası m Oztoprak* and Özlem Evren Kemer  \nMachine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery  \n[https://doi.org/10.1515/bmt-2023-0126](https://doi.org/10.1515/bmt-2023-0126)  \nReceived March 27, 2023; accepted February 28, 2024; published online March 18, 2024  \nAbstract  \nObjectives: In this study, we developed a machine learning approach for postoperative corneal endothelial cell images of patients who underwent Descemet’s membrane keratoplasty (DMEK) .  \nMethods: An AlexNet model is proposed and validated throughout the study for endothelial cell segmentation and cell location determination. The 506 images of postoperative corneal endothelial cells were analyzed. Endothelial cell detection, segmentation, and determining of its polygonal structure were identified. The proposed model is based on the training of an R-CNN to locate endothelial cells. Next, by determining the ridges separating adjacent cells, the density and hexagonality rates of DMEK patients are calculated. Results: The proposed method reached accuracy and F1 score rates of86.15 %and0.857, respectively, which indicates that it can reliably replace the manual detection of cells in vivo confocal microscopy (IVCM) . The AUC score of 0.764 from the proposed segmentation method suggests a satisfactory outcome.  \nConclusions: A model focused on segmenting endothelial cells can be employed to assess the health ofthe endothelium in DMEK patients.  \n*Corresponding author: Kasım Oztoprak, Department of Computer Engineering, Konya Food and Agriculture University, Beyşehir Cd., 42080 Meram, Konya, ","cbCaiebX9kPJOwTH","https://ap.wps.com/l/cbCaiebX9kPJOwTH","pdf",3114635,1,10,"English","en",105,"# Introduction\n# Methods\n## AlexNet model\n## R-CNN-based localization\n# Results\n## Segmentation performance\n# Conclusions","[{\"question\":\"What was the main objective of the study?\",\"answer\":\"To build a machine learning approach that analyzes postoperative corneal endothelial cell images for DMEK patients, focusing on reliable segmentation and localization of endothelial cells.\"},{\"question\":\"Which models were proposed and how were they used?\",\"answer\":\"An AlexNet model was used for endothelial cell segmentation and localization, with training based on an R-CNN to locate endothelial cells, followed by measuring density and hexagonality from ridges between adjacent cells.\"},{\"question\":\"How effective was the proposed method compared with manual detection?\",\"answer\":\"The method reached 86.15% accuracy and F1 of 0.857, with an AUC of 0.764, indicating it can reliably replace manual detection of cells in vivo using confocal microscopy.\"}]","Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery | 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was the main objective of the study?","Question",{"text":75,"@type":76},"To build a machine learning approach that analyzes postoperative corneal endothelial cell images for DMEK patients, focusing on reliable segmentation and localization of endothelial cells.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models were proposed and how were they used?",{"text":80,"@type":76},"An AlexNet model was used for endothelial cell segmentation and localization, with training based on an R-CNN to locate endothelial cells, followed by measuring density and hexagonality from ridges between adjacent cells.",{"name":82,"@type":73,"acceptedAnswer":83},"How effective was the proposed method compared with manual detection?",{"text":84,"@type":76},"The method reached 86.15% accuracy and F1 of 0.857, with an AUC of 0.764, indicating it can reliably replace manual detection of cells in vivo using confocal 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