Benchmarking commercial depth sensors for intraoperative markerless registration in neurosurgery applications

Benchmarking commercial depth sensors for intraoperative markerless registration in neurosurgery applications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Benchmarking commercial depth sensors for intraoperative markerless registration in neurosurgery applications Manuel Villa, Jaime Sancho, Gonzalo Rosa-Olmeda, Miguel Chavarrias, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5822013/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose : This study proposes a generalization of markerless patient registration in image-guided neurosurgery based on depth information. The work builds on previous research to evaluate the performance of a range of commercial depth cameras and two different registration algorithms in this context. Methods : A multimodal experimental setup was used, testing five depth cameras in seven configurations. Fiducial Registration Error (FRE) and Target Registration Error (TRE) metrics were calculated using Iterative Closest Point (ICP) and Deep Global Registration (DGR) algorithms. A phantom head model was used to simulate clinical conditions, with cameras positioned to capture the face and craniotomy regions. Results : The best-performing cameras, such as the D405 and Zed-M+, achieved TRE values as low as 2.36 ± 0.46 mm and 2.49 ± 0.35 mm, respectively, compared to manual registration that obtains a 1.37 mm error. Cameras equipped with texture projectors or enhanced depth refinement demonstrated improved performance. The proposed methodology effectively characterized the suitability of the camera for the registration tasks. Conclusions : This study validates an adaptable and reproducible framework to evaluate depth cameras in neurosurgical scenarios, highlighting D405 and Zed-M+ as reliable options. Future work will focus on improving depth quality through hardware and algorithmic improvements. The experimental data and the accompanying code were made publicly available to ensure reproducibility. Depth sensors Image registration MRI Computer-assisted intervention Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5822013","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":403563102,"identity":"5ab3c76b-3543-4694-b8d7-dc8d36f3056e","order_by":0,"name":"Manuel Villa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYNCCAgYGNnbmBhiX8QFhLQZALcyMcC3MBkRpYUDSwiaBT7Fu+9ljEh8MGBL7mBkbPzDusYnmlz58rJp3xzYGfv4DWLWYnclLk5wB1NLGzNgswfAsLXdmX1rabd4ztxkkZyRg13Igx0yax4DBGOQXCYYDh3M3nOExu83bdpvB4AZ2h5mdf2Mm/QeipfkHSMt+oJZikBb78zgcdgNoC9D7ckAtbRBbeHjMmMG2MOBw2I03xpY9BhJgLRYJB9JyZ5xhS5ace+Y2j8QNHFrO5xje+FFhwyPf3nz4xocDNrn9PcwHP7zdcVuOvx+7w6AAGhFwY4FxxINPPRaAiNZRMApGwSgYBQwArU9WJj2TgxYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7000-6289","institution":"CITSEM, Universidad Politécnica de Madrid","correspondingAuthor":true,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Villa","suffix":""},{"id":403563103,"identity":"468ff26d-0f55-40a8-8060-4005b06e8775","order_by":1,"name":"Jaime Sancho","email":"","orcid":"https://orcid.org/0000-0001-8767-6596","institution":"CITSEM, Universidad Politécnica de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Jaime","middleName":"","lastName":"Sancho","suffix":""},{"id":403563104,"identity":"24f43526-1e8c-4942-827e-c1e36759d69b","order_by":2,"name":"Gonzalo Rosa-Olmeda","email":"","orcid":"https://orcid.org/0000-0002-3236-1236","institution":"CITSEM, Universidad Politécnica de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Gonzalo","middleName":"","lastName":"Rosa-Olmeda","suffix":""},{"id":403563105,"identity":"d537a2f5-d5da-4397-baa7-0b24a3dc4403","order_by":3,"name":"Miguel Chavarrias","email":"","orcid":"https://orcid.org/0000-0003-0280-3440","institution":"CITSEM, Universidad Politécnica de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Chavarrias","suffix":""},{"id":403563106,"identity":"22fce880-960e-47e3-a0d2-d103033be818","order_by":4,"name":"Eduardo Juarez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFCCBCBmY2BgbADzbEjXkkaCFig4TFgDP3uO4eeKMhsG5hnJzz58+HM+cbvYAcaHP/Bokex5Yyx55lwaA+OMNOOZM3huJ+6cncBszINHi8GNHAPJxrbDQC0Jxsw8ErcTN9xOYJPG5zD7GznGPyFa0j8z/zE4B9LC/hOfwwwkcsygtuQYMzMkHADbwoDPYRJnnpVZNpxL42HseVPM2HMg2Xjn7MRmaXxa+NuTN99sKLORM2xP38zw44+d7Hbp5IMf8TkMBngMG2BOhcUqQSAPYxgQp34UjIJRMApGEAAA8vlNjZjBqSkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6096-1511","institution":"CITSEM, Universidad Politécnica de Madrid","correspondingAuthor":true,"prefix":"","firstName":"Eduardo","middleName":"","lastName":"Juarez","suffix":""},{"id":403563107,"identity":"82a6a194-d33a-427e-a517-2218fc40b5d5","order_by":5,"name":"Cesar Sanz","email":"","orcid":"https://orcid.org/0000-0002-2411-9132","institution":"CITSEM, Universidad Politécnica de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Cesar","middleName":"","lastName":"Sanz","suffix":""}],"badges":[],"createdAt":"2025-01-13 18:04:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5822013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5822013/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74211135,"identity":"57e6cb45-4c89-4cef-88cd-3c4917a88c57","added_by":"auto","created_at":"2025-01-20 05:05:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2142649,"visible":true,"origin":"","legend":"","description":"","filename":"CARS25Benchmarkingcommercialdepthsensorsforintraoperativemarkerlessregistrationinneurosurgeryapplications.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5822013/v1_covered_960e68dd-bca2-4539-87ca-8e57f3b8a4fc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBenchmarking commercial depth sensors for intraoperative markerless registration in neurosurgery applications\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidad Politécnica de Madrid","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Depth sensors, Image registration, MRI, Computer-assisted intervention","lastPublishedDoi":"10.21203/rs.3.rs-5822013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5822013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: This study proposes a generalization of markerless patient registration in image-guided neurosurgery based on depth information. The work builds on previous research to evaluate the performance of a range of commercial depth cameras and two different registration algorithms in this context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A multimodal experimental setup was used, testing five depth cameras in seven configurations. Fiducial Registration Error (FRE) and Target Registration Error (TRE) metrics were calculated using Iterative Closest Point (ICP) and Deep Global Registration (DGR) algorithms. A phantom head model was used to simulate clinical conditions, with cameras positioned to capture the face and craniotomy regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The best-performing cameras, such as the D405 and Zed-M+, achieved TRE values as low as 2.36 ± 0.46 mm and 2.49 ± 0.35 mm, respectively, compared to manual registration that obtains a 1.37 mm error. Cameras equipped with texture projectors or enhanced depth refinement demonstrated improved performance. The proposed methodology effectively characterized the suitability of the camera for the registration tasks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: This study validates an adaptable and reproducible framework to evaluate depth cameras in neurosurgical scenarios, highlighting D405 and Zed-M+ as reliable options. Future work will focus on improving depth quality through hardware and algorithmic improvements. The experimental data and the accompanying code were made publicly available to ensure reproducibility.\u003c/p\u003e","manuscriptTitle":"Benchmarking commercial depth sensors for intraoperative markerless registration in neurosurgery applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 04:56:56","doi":"10.21203/rs.3.rs-5822013/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"531cea5d-fbca-4b83-8359-82ad22d28ebc","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T04:56:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-20 04:56:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5822013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5822013","identity":"rs-5822013","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}