Slicer FSP: An Open‑Source Modular Platform for Reproducible Dental Implant Surgery Planning

Slicer FSP: An Open‑Source Modular Platform for Reproducible Dental Implant Surgery Planning | 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 Slicer FSP: An Open‑Source Modular Platform for Reproducible Dental Implant Surgery Planning Dimitris Trikeriotis, Aggelikh Theodoropoulou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8876559/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 To develop an open‑source application composed of specialized modules for oral and dental implant surgery planning, built on the medical imaging platform 3D Slicer. The goal was to integrate a fragmented set of tools into a unified, reproducible, and computationally coherent workflow. Methods Six Python scripted custom modules were developed and combined with three built‑in modules and five 3D Slicer extensions to form the Slicer–Free Surgical Planner (Slicer FSP). The system implements a complete digital workflow, from CBCT import and preprocessing to prosthetic simulation, implant planning, and surgical guide design. The architecture emphasizes modularity, interoperability, automated data handling, and standardized coordinate export to support downstream computational tasks. Results Compared with using multiple independent 3D Slicer modules, Slicer FSP significantly reduced workflow complexity and overall working time. The integrated design introduced new technical capabilities, including automated slice and implant coordinate export, enhanced alignment tools, top‑down implant simulation, improved data management, and a bilingual interface. These features increased reproducibility, reduced user‑dependent variability, and enabled consistent execution of complex planning steps. Conclusion Slicer FSP is an open, extensible, and transparent platform for oral surgery and implant planning, offering a technically robust alternative to commercial systems. Its open‑source availability supports further development, validation, and integration into research pipelines, facilitating broader adoption of reproducible, modular, and community‑driven digital workflows. Biomedical Engineering 3D Slicer Open-source software Dental implant planning CBCT Surgical guide design Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Static computer-assisted implant surgery (sCAIS) has been shown to provide significantly higher accuracy compared with conventional freehand implant placement techniques[ 1 – 6 ]. As a result, digital planning and surgical guides have become widely integrated into clinical practice. A large number of proprietary commercial software packages are available for implant planning and surgical guide fabrication. However, their use is associated with limitations such as high cost, lack of code transparency, and restricted ability to customize or extend functionality. Although free and open-source software (FOSS) could serve as a viable alternative, the relevant literature in dentistry remains limited and fragmented[ 7 – 15 ]. 3D Slicer is a free and open-source platform for visualization, processing, and analysis of medical and biomedical data, as well as for image-guided intervention planning[ 16 ]. A previous study investigated the use of 3D Slicer for implant planning and accuracy assessment. Although the results were consistent with the international literature, the required workflow was complex, time-consuming, and dependent on multiple modules[ 17 ]. The present study represents the first systematic customization of 3D Slicer for oral surgery planning, aiming to create an integrated, simplified, and extensible application for dental implant planning. Materials and Methods Slicer–Free Surgical Planner (Slicer FSP) was developed on the open-source platform 3D Slicer (version 5.10.0), following the official build guidelines. The development environment included Visual Studio Community 2022, Visual Studio Build Tools 2019, Qt 5.15.2, CMake 3.26.4, and NSIS 3.11. After cloning the 3D Slicer source code from GitHub, the custom modules were integrated into the application, and interface-level modifications were applied without altering core logic components. This ensured compatibility with the existing Slicer API and preserved full reproducibility. Slicer FSP was designed as a modular computational workflow combining six Python-scripted custom modules, three built-in modules (Markups, Segment Editor, Dynamic Modeler), five extensions (Dental Segmentator[ 10 , 18 ], SlicerIGT[ 19 ], SlicerMorph[ 20 – 21 ], SegmentEditorExtraEffects, SurfaceWrapSolidify[ 22 ]), and additional logic components such as Segment Statistics and SubjectHierarchyTreeView. All custom modules were implemented in Python 3.9 and adhere to the 3D Slicer 5.10.0 API. A unified top-level navigation menu and bilingual interface were added to standardize user interaction and reduce workflow fragmentation. The overall architecture forms a coherent pipeline that begins with CBCT preprocessing and ends with surgical guide design (Fig. 1). Module Overview 1. Oral Surgery Module Home Central navigation hub providing structured access to all Slicer FSP modules and predefined layouts. 2. Dental Implant Imaging CBCT preprocessing module supporting: DICOM metadata extraction Automated conversion of Dental Segmentator outputs to 3D models Cross-section curve creation with adjustable point density(Fig. 2) Slice-driven navigation with tilt control and coordinate export Multi-slice dynamic layouts with export to PNG/PDF(Fig. 3) Segment statistics via integrated logic components(Fig. 3) 3. Registration Module (Fig. 4) Implements two alignment strategies: Point-based registration Import scan-derived model Select ≥ 4 corresponding points Compute rigid transformation until RMSE ≤ 0.2 mm FastModelAlign (SlicerMorph) Rigid alignment without scaling Validation via point cloud subsampling Post-processing includes ACVD mesh repair, undercut detection, and generation of the GuideBase model (Fig. 5). 4. Virtual Prosthetics Provides prosthetic simulation with: Virtual tooth import and positioning(Fig. 6) Transformation export/import(Fig. 7) Full model management via SubjectHierarchyTreeView 5. Generic Implant Creator Implements implant creation and placement with: Parametric pre-implant generation(Fig. 8) Dynamic orientation via platform–apex points Automated implant selection by jaw/brand/system(Fig. 9) Coordinate export/import for reproducible planning Full implant and ring management via SubjectHierarchyTreeView 6. Oral Surgery Data Centralized data management module organizing: Implants Models, points, curves Segments All properties are editable to support structured workflows. A summary of module capabilities is provided in Table 1. Surgical Guide Design Workflow (Fig. 10) Guide fabrication is performed using Dynamic Modeler and Segment Editor tools: Closed Curve definition on GuideBase CurveCut extraction and Hollow operation (3 mm shell) Surface refinement (Closing, Logical Operators, Scissors, Median smoothing) Export to STL/OBJ Results Slicer-FSP was successfully developed. Its structure and workflow enable faster and simpler procedures within the complex 3D Slicer environment. New functionalities include: Export of slice and implant coordinates Top-down implant planning Unified multi-slice layout Multiple alignment methods Automated segmentation Free export of generated models in various formats Expandable prosthetic and implant lists Bilingual interface Enhanced data visualization and management Implant planning time per jaw ranged from 30–90 minutes, depending on case complexity. Processing time depends on hardware; on the development laptop (i7-12700H 2.3 GHz, 32 GB RAM, NVIDIA RTX A1000 4 GB), automatic segmentation required less than 10 minutes. The application is freely available at ‘ https://freesurgplan.edu.gr/ ’ with three download options: (1) executable (Slicer FSP), (2) Python-scripted custom modules, (3) build source code. The website also provides step-by-step tutorial videos covering the entire workflow. The first release currently supports Windows; macOS and Linux support will follow. Discussion Commercial dental software has accelerated the adoption of digital technologies in implantology. However, high cost, closed-source architecture, and limited customizability restrict accessibility and reduce the clinical feedback essential for advancing digital methods. This study demonstrates the feasibility of developing a specialized implant-planning environment entirely within 3D Slicer, leveraging its modular architecture and extensible API. Slicer FSP consolidates multiple independent modules, extensions, and manual steps into a unified computational pipeline. This integration reduces workflow fragmentation, minimizes user-dependent variability, and enables consistent execution of complex planning tasks. The six custom modules interact directly with built-in logic components and external extensions, forming a coherent data flow from CBCT preprocessing to surgical guide design. Exporting DICOM metadata, slice coordinates, and platform–apex coodinates provides structured datasets suitable for downstream computational research, including machine-learning models for automated implant-position prediction[ 23 ]. Prosthetic simulation preceding implant insertion ensures top-down planning[ 24 ]. Unlike commercial systems that determine the optimal slice after implant adjustment (implant-driven), Slicer FSP selects the optimal slice orientation first (slice-driven), offering more comprehensive 3D control. A key advantage of 3D Slicer is the ability to import multiple CBCT scans, enabling comparative measurements such as postoperative assessments. Strengths Slicer FSP consolidates previously fragmented steps into a unified computational workflow, reducing complexity and enabling consistent execution of implant-planning tasks. Its open-source architecture ensures full transparency, reproducibility, and independent verification, while interoperability with built-in 3D Slicer logic components and widely used extensions enhances flexibility. Automated coordinate export provides structured geometric data suitable for downstream computational research, including machine-learning applications. The modular design supports extensibility, allowing users to expand prosthetic and implant libraries and adapt the system to diverse research or educational needs. Top-down planning, slice-driven navigation, and predefined layouts help reduce user-dependent variability. Limitations As with 3D Slicer, Slicer FSP is intended for research and educational use rather than clinical deployment. Users unfamiliar with medical-imaging software may initially encounter a learning curve, although guided layouts mitigate this. Performance is hardware-dependent, particularly for segmentation and rendering tasks. The current release supports Windows only, with macOS and Linux versions planned. Some functionalities rely on external extensions that may evolve independently. Finally, although the system supports coordinate export and model comparison, quantitative accuracy-validation tools are not embedded directly into the workflow and require external analysis. Future Work Future development of Slicer FSP will focus on expanding its computational capabilities and improving interoperability within the 3D Slicer ecosystem. Integration of quantitative accuracy-assessment tools, including automated deviation analysis and implant–bone proximity metrics, would enable direct validation of planning outcomes within the platform. Additional work will explore the incorporation of machine-learning models for automated implant-position prediction, leveraging the standardized coordinate exports already implemented in the system. Cross-platform support for macOS and Linux is planned to broaden accessibility and facilitate adoption in diverse research environments. Enhancing real-time rendering performance and optimizing segmentation workflows for lower-performance hardware will further improve usability. Finally, future versions may incorporate customizable surgical-guide templates, parametric guide-tube generation, and automated quality-control checks to support more advanced and fully integrated digital workflows. Conclusion Slicer FSP represents a structured and extensible open-source platform for oral and dental implant surgery planning, built as an integrated suite of modules within the 3D Slicer ecosystem. Rather than functioning as a standalone application, it operates as a coordinated computational workflow that leverages and extends existing Slicer capabilities. The system streamlines CBCT processing, prosthetic simulation, implant planning, and surgical-guide design while introducing new tools for alignment, coordinate export, and data management. Although primarily intended for research and educational use, Slicer FSP provides a technically robust alternative to commercial solutions and supports reproducible, transparent, and community-driven development. Its open availability enables further refinement, validation, and integration into advanced computational pipelines, including AI-assisted planning and automated surgical workflows. Declarations Code Availability To ensure full transparency and reproducibility, the complete Slicer-FSP project—including the source code, custom modules, modified 3D Slicer files, and the packaged executable—is openly available on GitHub ( https://github.com/dnt102/Slicer-FSP ) and permanently archived on Zenodo under a concept DOI ( https://doi.org/10.5281/zenodo.18596636 ), which always points to the latest version. References Varga E Jr, Antal M, Major L, Kiscsatári R, Braunitzer G, Piffkó J (2020) Guidance means accuracy: A randomized clinical trial on freehand versus guided dental implantation. Clin Oral Implants Res 31(5):417–430. https://doi.org/10.1111/clr.13585 Smitkarn P, Subbalekha K, Mattheos N, Pimkhaokham A (2019) The accuracy of single-tooth implants placed using fully digital-guided surgery and freehand implant surgery. J Clin Periodontol 46(9):949–957. https://doi.org/10.1111/jcpe.13162 Schneider D, Sancho-Puchades M, Mir-Marí J, Mühlemann S, Jung R, Hämmerle C (2019) A randomized controlled clinical trial comparing conventional and computer-assisted implant planning and placement in partially edentulous patients. Part 4: Accuracy of implant placement. Int J Periodontics Restor Dent 39(4):e111–e122. https://doi.org/10.11607/prd.4133 Younes F, Cosyn J, De Bruyckere T, Cleymaet R, Bouckaert E, Eghbali A (2018) Accuracy of freehand, pilot-drill guided, and fully guided implant surgery in partially edentulous patients: A randomized controlled study. J Clin Periodontol 45(6):721–732. https://doi.org/10.1111/jcpe.12894 Vercruyssen M, Cox C, Naert I, Jacobs R, Teughels W, Quirynen M (2016) Accuracy and patient-centered outcome variables in guided implant surgery: A randomized clinical trial comparing immediate with delayed loading. Clin Oral Implants Res 27(4):427–432. https://doi.org/10.1111/clr.12571 Dreiseidler T, Neugebauer J, Ritter L, Lingohr T, Rothamel D, Mischkowski RA, Zöller JE (2009) Accuracy of a newly developed integrated system for dental implant planning. Clin Oral Implants Res 20(11):1191–1199. https://doi.org/10.1111/j.1600-0501.2009.01786.x Chruściel-Nogalska M, Smektała T, Tutak M, Sporniak-Tutak K, Olszewski R (2017) Open-source software in dentistry: A systematic review. Int J Technol Assess Health Care 33(4):487–493. https://doi.org/10.1017/S0266462317000700 Villoria EM, Lenzi AR, Soares RV et al (2017) Post-processing open-source software for CBCT monitoring of periapical lesion healing: Technical report of two cases. Dentomaxillofac Radiol 46(1):20160293. https://doi.org/10.1259/dmfr.20160293 Talmazov G, Bencharit S, Waldrop TC, Ammoun R (2020) Accuracy of implant placement position using non-dental open-source software: An in vitro study. J Prosthodont. https://doi.org/10.1111/jopr.13274 Dot G et al (2024) DentalSegmentator: Robust open-source deep learning-based CT and CBCT image segmentation. J Dent. https://doi.org/10.1016/j.jdent.2024.105123 De Stefano A (2021) Unilateral condylar hyperplasia: A 3D CBCT morphometric and volumetric evaluation using open-source software. Int J Morphol 39(1):1–8. https://doi.org/10.4067/S0717-95022021000100001 Cirrincione C, Guarnieri G, Morelli A (2025) Digital workflow with open-source CAD-CAM software for designing a customized 3D laser-printed titanium mesh for guided bone regeneration. Bioengineering 12:436. https://doi.org/10.3390/bioengineering12040436 Mayasari D, Kurniatie M, Prasetyanto WA et al (2025) Development of a surgical guide using open-source software for dental implant placement. Maj Ked Gigi Indones 11:1. https://doi.org/10.22146/majkedgiind.96091 Farook TH, Twinkle A, Abdullah J, Jamayet N (2020) Optimization of prosthodontic CAD models: A virtual evaluation of mesh-quality reduction using open-source software. J Prosthodont 30. https://doi.org/10.1111/jopr.13200 Imtiaz S, Imtiaz S, Usman M, Ikram N (2024) A tertiary study on open-source software research. IEEE Access 12:189952–189993. https://doi.org/10.1109/ACCESS.2024.3514075 Kikinis R, Pieper SD, Vosburgh KG (2014) 3D Slicer: A platform for subject-specific image analysis, visualization, and clinical support. Intraoperative Imaging and Image-Guided Therapy. Springer, pp 277–289. https://doi.org/10.1007/978-1-4614-7657-3_19 Trikeriotis D, Theodoropoulou A, Mountricha M (2021) Dental implant planning, guide design and accuracy evaluation using free and open-source software: An in vivo study. Int J CARS 16(S1):26–27. https://doi.org/10.1007/s11548-021-02375-4 Isensee F et al (2021) nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203–211. https://doi.org/10.1038/s41592-020-01008-z Ungi T, Lasso A, Fichtinger G (2016) Open-source platforms for navigated image-guided interventions. Med Image Anal 33:181–186. https://doi.org/10.1016/j.media.2016.06.011 Rolfe S, Pieper S, Porto A et al (2021) SlicerMorph: An open and extensible platform to retrieve, visualize, and analyze 3D morphology. Methods Ecol Evol 12:1816–1825. https://doi.org/10.1111/2041-210X.13669 Porto A, Rolfe SM, Maga AM (2021) ALPACA: A fast and accurate approach for automated landmarking of 3D biological structures. Methods Ecol Evol 12:2129–2144. https://doi.org/10.1111/2041-210X.13754 Weidert S, Andreß S, Linhart C et al (2020) 3D printing method for next-day acetabular fracture surgery using a surface-filtering pipeline: Feasibility and 1-year clinical results. Int J Comput Assist Radiol Surg 15. https://doi.org/10.1007/s11548-020-02165-3 Yang X, Li X, Zheng M et al (2026) RegFreeNet: A registration-free network for CBCT-based 3D dental implant planning. https://doi.org/10.48550/arXiv.2601.14703 Jorba-García A, Pozzi A, Chen Z et al (2025) Glossary of computer-assisted implant surgery and related terms. Clin Exp Dent Res 11. https://doi.org/10.1002/cre2.XXXX Table 1 Table 1 is available in the Supplementary Files. Additional Declarations The authors declare no competing interests. Supplementary Files Table.doc 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-8876559","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591194456,"identity":"fadab781-a281-403b-bf11-6e253f9726d2","order_by":0,"name":"Dimitris Trikeriotis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACNgbGBwwfQCwJxgZitTAbMM4AMniI1sIA1MLMA9ZCrAY+6WbGxza/7sjbSzc3Pi5gqM0n7DCZw8zGuX3PDHtkDjYbz2A4bknQeWwS+cekc3sOM/ZIJLZJ8zAcMyBsi0Qym7Rlz2F7ErUw/DicCNVSQ5QWZsPehsPJPTcSm415DA4Q1iI/I5nxwY8/h23bZ6Q/fMxTUUdYCxgwtsFYBoeJ08HA8AfOqiNWyygYBaNgFIwgAACJeTX++Xxk+QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0000-6786-6624","institution":"Independent researcher","correspondingAuthor":true,"prefix":"","firstName":"Dimitris","middleName":"","lastName":"Trikeriotis","suffix":""},{"id":591194457,"identity":"5e1c8d2e-a609-4352-94c8-775eea49b2d6","order_by":1,"name":"Aggelikh Theodoropoulou","email":"","orcid":"","institution":"Independent researcher","correspondingAuthor":false,"prefix":"","firstName":"Aggelikh","middleName":"","lastName":"Theodoropoulou","suffix":""}],"badges":[],"createdAt":"2026-02-14 03:24:38","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-8876559/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8876559/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102897341,"identity":"cc0e4923-1b56-486a-9f95-842089e7b01d","added_by":"auto","created_at":"2026-02-18 06:56:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85281,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow diagram of the Slicer‑FSP and the connections between scripted modules, built‑in modules, and extensions. The central module links, data links, and overall workflow direction are illustrated.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/b5d501812a9a5a6de1dc4bf8.png"},{"id":102897372,"identity":"8779f209-711b-4b02-80a5-4137bdf1aec4","added_by":"auto","created_at":"2026-02-18 06:56:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2407655,"visible":true,"origin":"","legend":"\u003cp\u003eCurve point‑density adjustment. \u003cstrong\u003ea\u003c/strong\u003e Resample distance 3 mm (102 points). \u003cstrong\u003eb\u003c/strong\u003e Resample distance 0.3 mm (337 points).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/75abbee44a06f89aa81bd3c2.png"},{"id":102897347,"identity":"9d92e1e6-cd8d-4a7d-9962-74ea9a992770","added_by":"auto","created_at":"2026-02-18 06:56:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3976238,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic multi‑slice layout displaying one slice per selected curve position during slice‑driven navigation. Inset: clinical image after removal of the cystic lesion.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/cb15b8b2b35cc072bdc43e95.png"},{"id":102897396,"identity":"98ecf349-c6e2-4ba6-836a-6decd3bd19dc","added_by":"auto","created_at":"2026-02-18 06:57:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3133026,"visible":true,"origin":"","legend":"\u003cp\u003eAlignment options. \u003cstrong\u003ea\u003c/strong\u003e Point‑based registration with deviation control. \u003cstrong\u003eb\u003c/strong\u003ePoint‑cloud model matching. \u003cstrong\u003ea↔b\u003c/strong\u003e Comparison of the results of the two methods.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/fb49dc80d66f8802e96faa6d.png"},{"id":102897364,"identity":"b5d9b9c5-4e05-45b8-9090-75258add4d80","added_by":"auto","created_at":"2026-02-18 06:56:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1997461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Aligned scan‑derived model. \u003cstrong\u003eb\u003c/strong\u003ePost‑processing of the aligned model. \u003cstrong\u003ec\u003c/strong\u003e Undercut detection (red). \u003cstrong\u003ed\u003c/strong\u003eUndercut exclusion (green). \u003cstrong\u003ee\u003c/strong\u003e Final model (GuideBase) for surgical guide design.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/5720c767202ababbb2a74c30.png"},{"id":102897358,"identity":"1bdcf948-0aed-4039-9380-ddbfeafe2007","added_by":"auto","created_at":"2026-02-18 06:56:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3702896,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of prosthetic components from a customizable list and simulation at potential implant slice‑positions.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/e29e67acd9887dd490a9258a.png"},{"id":102897383,"identity":"7c722831-efe1-4fea-bff7-a7e28e9120a9","added_by":"auto","created_at":"2026-02-18 06:56:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2332217,"visible":true,"origin":"","legend":"\u003cp\u003eImplant site determination based on slice‑driven navigation and prosthetic simulation, with dynamic visualization of the selected positions.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/9e2e5a6260aae742e295939b.png"},{"id":102897351,"identity":"4c5af6c5-2c6d-4932-951b-cb85de6659d6","added_by":"auto","created_at":"2026-02-18 06:56:30","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2429787,"visible":true,"origin":"","legend":"\u003cp\u003eCreation and evaluation of pre‑implants at the selected positions, with appropriate dimensions and orientation.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/9bf52620a08a938f45b4ad0a.png"},{"id":102897381,"identity":"3a8f871b-0207-45f4-a801-d58255500f16","added_by":"auto","created_at":"2026-02-18 06:56:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2553832,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of an implant from a customizable list and alignment with the pre‑implant. The example shows an MIS‑7 implant, but any implant system can be added.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/c41412f7ed96d69e436e6e09.png"},{"id":102897354,"identity":"bfe5e188-e5c1-4a85-ace5-cadbc6d78909","added_by":"auto","created_at":"2026-02-18 06:56:31","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2256941,"visible":true,"origin":"","legend":"\u003cp\u003eSurgical guide design workflow. \u003cstrong\u003ea\u003c/strong\u003e Closed curve definition on GuideBase. \u003cstrong\u003eb\u003c/strong\u003eCurveCut extraction. \u003cstrong\u003ec\u003c/strong\u003e Hollow operation (3 mm shell). \u003cstrong\u003ed\u003c/strong\u003e Shell refinement and boolean ring integration. \u003cstrong\u003ee\u003c/strong\u003e Final smoothing. \u003cstrong\u003ef\u003c/strong\u003eExport to STL/OBJ.\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/8f7a82010de1f78cbd16a6a7.png"},{"id":104834870,"identity":"1492ab2f-7f60-49a0-88a9-8e06f479ed25","added_by":"auto","created_at":"2026-03-17 17:33:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":31681545,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/61538c19-6024-454a-99f2-2f5a2dd66004.pdf"},{"id":102932202,"identity":"aed61e0d-c838-4dba-a2a4-ac14825f32fc","added_by":"auto","created_at":"2026-02-18 15:15:13","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32768,"visible":true,"origin":"","legend":"","description":"","filename":"Table.doc","url":"https://assets-eu.researchsquare.com/files/rs-8876559/v1/6fea397a54b315b6b378d3bf.doc"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSlicer FSP: An Open‑Source Modular Platform for Reproducible Dental Implant Surgery Planning\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStatic computer-assisted implant surgery (sCAIS) has been shown to provide significantly higher accuracy compared with conventional freehand implant placement techniques[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As a result, digital planning and surgical guides have become widely integrated into clinical practice.\u003c/p\u003e \u003cp\u003eA large number of proprietary commercial software packages are available for implant planning and surgical guide fabrication. However, their use is associated with limitations such as high cost, lack of code transparency, and restricted ability to customize or extend functionality. Although free and open-source software (FOSS) could serve as a viable alternative, the relevant literature in dentistry remains limited and fragmented[\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e3D Slicer is a free and open-source platform for visualization, processing, and analysis of medical and biomedical data, as well as for image-guided intervention planning[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A previous study investigated the use of 3D Slicer for implant planning and accuracy assessment. Although the results were consistent with the international literature, the required workflow was complex, time-consuming, and dependent on multiple modules[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study represents the first systematic customization of 3D Slicer for oral surgery planning, aiming to create an integrated, simplified, and extensible application for dental implant planning.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eSlicer\u0026ndash;Free Surgical Planner (Slicer FSP) was developed on the open-source platform 3D Slicer (version 5.10.0), following the official build guidelines. The development environment included Visual Studio Community 2022, Visual Studio Build Tools 2019, Qt 5.15.2, CMake 3.26.4, and NSIS 3.11. After cloning the 3D Slicer source code from GitHub, the custom modules were integrated into the application, and interface-level modifications were applied without altering core logic components. This ensured compatibility with the existing Slicer API and preserved full reproducibility.\u003c/p\u003e \u003cp\u003eSlicer FSP was designed as a modular computational workflow combining six Python-scripted custom modules, three built-in modules (Markups, Segment Editor, Dynamic Modeler), five extensions (Dental Segmentator[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], SlicerIGT[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], SlicerMorph[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], SegmentEditorExtraEffects, SurfaceWrapSolidify[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]), and additional logic components such as Segment Statistics and SubjectHierarchyTreeView. All custom modules were implemented in Python 3.9 and adhere to the 3D Slicer 5.10.0 API. A unified top-level navigation menu and bilingual interface were added to standardize user interaction and reduce workflow fragmentation. The overall architecture forms a coherent pipeline that begins with CBCT preprocessing and ends with surgical guide design (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eModule Overview\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e1. Oral Surgery Module Home\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCentral navigation hub providing structured access to all Slicer FSP modules and predefined layouts.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Dental Implant Imaging\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCBCT preprocessing module supporting:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDICOM metadata extraction\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAutomated conversion of Dental Segmentator outputs to 3D models\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCross-section curve creation with adjustable point density(Fig.\u0026nbsp;2)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSlice-driven navigation with tilt control and coordinate export\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMulti-slice dynamic layouts with export to PNG/PDF(Fig.\u0026nbsp;3)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSegment statistics via integrated logic components(Fig.\u0026nbsp;3)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Registration Module\u003c/b\u003e (Fig.\u0026nbsp;4)\u003c/p\u003e \u003cp\u003eImplements two alignment strategies:\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePoint-based registration\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eImport scan-derived model\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSelect\u0026thinsp;\u0026ge;\u0026thinsp;4 corresponding points\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCompute rigid transformation until RMSE\u0026thinsp;\u0026le;\u0026thinsp;0.2 mm\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFastModelAlign (SlicerMorph)\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRigid alignment without scaling\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eValidation via point cloud subsampling\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003ePost-processing includes ACVD mesh repair, undercut detection, and generation of the GuideBase model (Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003cb\u003e4. Virtual Prosthetics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eProvides prosthetic simulation with:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eVirtual tooth import and positioning(Fig.\u0026nbsp;6)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTransformation export/import(Fig.\u0026nbsp;7)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFull model management via SubjectHierarchyTreeView\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e5. Generic Implant Creator\u003c/b\u003e \u003c/p\u003e \u003cp\u003eImplements implant creation and placement with:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eParametric pre-implant generation(Fig.\u0026nbsp;8)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDynamic orientation via platform\u0026ndash;apex points\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAutomated implant selection by jaw/brand/system(Fig.\u0026nbsp;9)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCoordinate export/import for reproducible planning\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFull implant and ring management via SubjectHierarchyTreeView\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e6. Oral Surgery Data\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCentralized data management module organizing:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eImplants\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModels, points, curves\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSegments\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAll properties are editable to support structured workflows.\u003c/p\u003e \u003cp\u003eA summary of module capabilities is provided in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSurgical Guide Design Workflow\u003c/b\u003e(Fig.\u0026nbsp;10)\u003c/p\u003e \u003cp\u003eGuide fabrication is performed using Dynamic Modeler and Segment Editor tools:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eClosed Curve definition on GuideBase\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCurveCut extraction and Hollow operation (3 mm shell)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSurface refinement (Closing, Logical Operators, Scissors, Median smoothing)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExport to STL/OBJ\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSlicer-FSP was successfully developed. Its structure and workflow enable faster and simpler procedures within the complex 3D Slicer environment. New functionalities include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eExport of slice and implant coordinates\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTop-down implant planning\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnified multi-slice layout\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMultiple alignment methods\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAutomated segmentation\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFree export of generated models in various formats\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExpandable prosthetic and implant lists\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBilingual interface\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEnhanced data visualization and management\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eImplant planning time per jaw ranged from 30\u0026ndash;90 minutes, depending on case complexity. Processing time depends on hardware; on the development laptop (i7-12700H 2.3 GHz, 32 GB RAM, NVIDIA RTX A1000 4 GB), automatic segmentation required less than 10 minutes.\u003c/p\u003e \u003cp\u003eThe application is freely available at \u0026lsquo;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://freesurgplan.edu.gr/\u003c/span\u003e\u003cspan address=\"https://freesurgplan.edu.gr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026rsquo; with three download options: (1) executable (Slicer FSP), (2) Python-scripted custom modules, (3) build source code.\u003c/p\u003e \u003cp\u003eThe website also provides step-by-step tutorial videos covering the entire workflow. The first release currently supports Windows; macOS and Linux support will follow.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCommercial dental software has accelerated the adoption of digital technologies in implantology. However, high cost, closed-source architecture, and limited customizability restrict accessibility and reduce the clinical feedback essential for advancing digital methods. This study demonstrates the feasibility of developing a specialized implant-planning environment entirely within 3D Slicer, leveraging its modular architecture and extensible API.\u003c/p\u003e \u003cp\u003eSlicer FSP consolidates multiple independent modules, extensions, and manual steps into a unified computational pipeline. This integration reduces workflow fragmentation, minimizes user-dependent variability, and enables consistent execution of complex planning tasks. The six custom modules interact directly with built-in logic components and external extensions, forming a coherent data flow from CBCT preprocessing to surgical guide design.\u003c/p\u003e \u003cp\u003eExporting DICOM metadata, slice coordinates, and platform\u0026ndash;apex coodinates provides structured datasets suitable for downstream computational research, including machine-learning models for automated implant-position prediction[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Prosthetic simulation preceding implant insertion ensures top-down planning[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Unlike commercial systems that determine the optimal slice after implant adjustment (implant-driven), Slicer FSP selects the optimal slice orientation first (slice-driven), offering more comprehensive 3D control. A key advantage of 3D Slicer is the ability to import multiple CBCT scans, enabling comparative measurements such as postoperative assessments.\u003c/p\u003e\n\u003ch3\u003eStrengths\u003c/h3\u003e\n\u003cp\u003eSlicer FSP consolidates previously fragmented steps into a unified computational workflow, reducing complexity and enabling consistent execution of implant-planning tasks. Its open-source architecture ensures full transparency, reproducibility, and independent verification, while interoperability with built-in 3D Slicer logic components and widely used extensions enhances flexibility. Automated coordinate export provides structured geometric data suitable for downstream computational research, including machine-learning applications. The modular design supports extensibility, allowing users to expand prosthetic and implant libraries and adapt the system to diverse research or educational needs. Top-down planning, slice-driven navigation, and predefined layouts help reduce user-dependent variability.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eAs with 3D Slicer, Slicer FSP is intended for research and educational use rather than clinical deployment. Users unfamiliar with medical-imaging software may initially encounter a learning curve, although guided layouts mitigate this. Performance is hardware-dependent, particularly for segmentation and rendering tasks. The current release supports Windows only, with macOS and Linux versions planned. Some functionalities rely on external extensions that may evolve independently. Finally, although the system supports coordinate export and model comparison, quantitative accuracy-validation tools are not embedded directly into the workflow and require external analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFuture Work\u003c/h3\u003e\n\u003cp\u003eFuture development of Slicer FSP will focus on expanding its computational capabilities and improving interoperability within the 3D Slicer ecosystem. Integration of quantitative accuracy-assessment tools, including automated deviation analysis and implant\u0026ndash;bone proximity metrics, would enable direct validation of planning outcomes within the platform. Additional work will explore the incorporation of machine-learning models for automated implant-position prediction, leveraging the standardized coordinate exports already implemented in the system.\u003c/p\u003e \u003cp\u003eCross-platform support for macOS and Linux is planned to broaden accessibility and facilitate adoption in diverse research environments. Enhancing real-time rendering performance and optimizing segmentation workflows for lower-performance hardware will further improve usability. Finally, future versions may incorporate customizable surgical-guide templates, parametric guide-tube generation, and automated quality-control checks to support more advanced and fully integrated digital workflows.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSlicer FSP represents a structured and extensible open-source platform for oral and dental implant surgery planning, built as an integrated suite of modules within the 3D Slicer ecosystem. Rather than functioning as a standalone application, it operates as a coordinated computational workflow that leverages and extends existing Slicer capabilities. The system streamlines CBCT processing, prosthetic simulation, implant planning, and surgical-guide design while introducing new tools for alignment, coordinate export, and data management.\u003c/p\u003e \u003cp\u003eAlthough primarily intended for research and educational use, Slicer FSP provides a technically robust alternative to commercial solutions and supports reproducible, transparent, and community-driven development. Its open availability enables further refinement, validation, and integration into advanced computational pipelines, including AI-assisted planning and automated surgical workflows.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCode Availability\u003c/h2\u003e \u003cp\u003eTo ensure full transparency and reproducibility, the complete Slicer-FSP project\u0026mdash;including the source code, custom modules, modified 3D Slicer files, and the packaged executable\u0026mdash;is openly available on GitHub (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/dnt102/Slicer-FSP\u003c/span\u003e\u003cspan address=\"https://github.com/dnt102/Slicer-FSP\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and permanently archived on Zenodo under a concept DOI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.18596636\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.18596636\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which always points to the latest version.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVarga E Jr, Antal M, Major L, Kiscsat\u0026aacute;ri R, Braunitzer G, Piffk\u0026oacute; J (2020) Guidance means accuracy: A randomized clinical trial on freehand versus guided dental implantation. Clin Oral Implants Res 31(5):417\u0026ndash;430. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/clr.13585\u003c/span\u003e\u003cspan address=\"10.1111/clr.13585\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmitkarn P, Subbalekha K, Mattheos N, Pimkhaokham A (2019) The accuracy of single-tooth implants placed using fully digital-guided surgery and freehand implant surgery. J Clin Periodontol 46(9):949\u0026ndash;957. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jcpe.13162\u003c/span\u003e\u003cspan address=\"10.1111/jcpe.13162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneider D, Sancho-Puchades M, Mir-Mar\u0026iacute; J, M\u0026uuml;hlemann S, Jung R, H\u0026auml;mmerle C (2019) A randomized controlled clinical trial comparing conventional and computer-assisted implant planning and placement in partially edentulous patients. Part 4: Accuracy of implant placement. Int J Periodontics Restor Dent 39(4):e111\u0026ndash;e122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11607/prd.4133\u003c/span\u003e\u003cspan address=\"10.11607/prd.4133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYounes F, Cosyn J, De Bruyckere T, Cleymaet R, Bouckaert E, Eghbali A (2018) Accuracy of freehand, pilot-drill guided, and fully guided implant surgery in partially edentulous patients: A randomized controlled study. J Clin Periodontol 45(6):721\u0026ndash;732. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jcpe.12894\u003c/span\u003e\u003cspan address=\"10.1111/jcpe.12894\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVercruyssen M, Cox C, Naert I, Jacobs R, Teughels W, Quirynen M (2016) Accuracy and patient-centered outcome variables in guided implant surgery: A randomized clinical trial comparing immediate with delayed loading. Clin Oral Implants Res 27(4):427\u0026ndash;432. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/clr.12571\u003c/span\u003e\u003cspan address=\"10.1111/clr.12571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDreiseidler T, Neugebauer J, Ritter L, Lingohr T, Rothamel D, Mischkowski RA, Z\u0026ouml;ller JE (2009) Accuracy of a newly developed integrated system for dental implant planning. Clin Oral Implants Res 20(11):1191\u0026ndash;1199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1600-0501.2009.01786.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0501.2009.01786.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChruściel-Nogalska M, Smektała T, Tutak M, Sporniak-Tutak K, Olszewski R (2017) Open-source software in dentistry: A systematic review. Int J Technol Assess Health Care 33(4):487\u0026ndash;493. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0266462317000700\u003c/span\u003e\u003cspan address=\"10.1017/S0266462317000700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVilloria EM, Lenzi AR, Soares RV et al (2017) Post-processing open-source software for CBCT monitoring of periapical lesion healing: Technical report of two cases. Dentomaxillofac Radiol 46(1):20160293. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1259/dmfr.20160293\u003c/span\u003e\u003cspan address=\"10.1259/dmfr.20160293\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalmazov G, Bencharit S, Waldrop TC, Ammoun R (2020) Accuracy of implant placement position using non-dental open-source software: An in vitro study. J Prosthodont. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jopr.13274\u003c/span\u003e\u003cspan address=\"10.1111/jopr.13274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDot G et al (2024) DentalSegmentator: Robust open-source deep learning-based CT and CBCT image segmentation. J Dent. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jdent.2024.105123\u003c/span\u003e\u003cspan address=\"10.1016/j.jdent.2024.105123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Stefano A (2021) Unilateral condylar hyperplasia: A 3D CBCT morphometric and volumetric evaluation using open-source software. Int J Morphol 39(1):1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4067/S0717-95022021000100001\u003c/span\u003e\u003cspan address=\"10.4067/S0717-95022021000100001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCirrincione C, Guarnieri G, Morelli A (2025) Digital workflow with open-source CAD-CAM software for designing a customized 3D laser-printed titanium mesh for guided bone regeneration. Bioengineering 12:436. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/bioengineering12040436\u003c/span\u003e\u003cspan address=\"10.3390/bioengineering12040436\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayasari D, Kurniatie M, Prasetyanto WA et al (2025) Development of a surgical guide using open-source software for dental implant placement. Maj Ked Gigi Indones 11:1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22146/majkedgiind.96091\u003c/span\u003e\u003cspan address=\"10.22146/majkedgiind.96091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarook TH, Twinkle A, Abdullah J, Jamayet N (2020) Optimization of prosthodontic CAD models: A virtual evaluation of mesh-quality reduction using open-source software. J Prosthodont 30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jopr.13200\u003c/span\u003e\u003cspan address=\"10.1111/jopr.13200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImtiaz S, Imtiaz S, Usman M, Ikram N (2024) A tertiary study on open-source software research. IEEE Access 12:189952\u0026ndash;189993. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/ACCESS.2024.3514075\u003c/span\u003e\u003cspan address=\"10.1109/ACCESS.2024.3514075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKikinis R, Pieper SD, Vosburgh KG (2014) 3D Slicer: A platform for subject-specific image analysis, visualization, and clinical support. Intraoperative Imaging and Image-Guided Therapy. Springer, pp 277\u0026ndash;289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4614-7657-3_19\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4614-7657-3_19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrikeriotis D, Theodoropoulou A, Mountricha M (2021) Dental implant planning, guide design and accuracy evaluation using free and open-source software: An in vivo study. Int J CARS 16(S1):26\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11548-021-02375-4\u003c/span\u003e\u003cspan address=\"10.1007/s11548-021-02375-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsensee F et al (2021) nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203\u0026ndash;211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41592-020-01008-z\u003c/span\u003e\u003cspan address=\"10.1038/s41592-020-01008-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUngi T, Lasso A, Fichtinger G (2016) Open-source platforms for navigated image-guided interventions. Med Image Anal 33:181\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.media.2016.06.011\u003c/span\u003e\u003cspan address=\"10.1016/j.media.2016.06.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRolfe S, Pieper S, Porto A et al (2021) SlicerMorph: An open and extensible platform to retrieve, visualize, and analyze 3D morphology. Methods Ecol Evol 12:1816\u0026ndash;1825. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.13669\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.13669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorto A, Rolfe SM, Maga AM (2021) ALPACA: A fast and accurate approach for automated landmarking of 3D biological structures. Methods Ecol Evol 12:2129\u0026ndash;2144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.13754\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.13754\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeidert S, Andre\u0026szlig; S, Linhart C et al (2020) 3D printing method for next-day acetabular fracture surgery using a surface-filtering pipeline: Feasibility and 1-year clinical results. Int J Comput Assist Radiol Surg 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11548-020-02165-3\u003c/span\u003e\u003cspan address=\"10.1007/s11548-020-02165-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Li X, Zheng M et al (2026) RegFreeNet: A registration-free network for CBCT-based 3D dental implant planning. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arXiv.2601.14703\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.2601.14703\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJorba-Garc\u0026iacute;a A, Pozzi A, Chen Z et al (2025) Glossary of computer-assisted implant surgery and related terms. Clin Exp Dent Res 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cre2.XXXX\u003c/span\u003e\u003cspan address=\"10.1002/cre2.XXXX\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"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":"3D Slicer; Open-source software; Dental implant planning; CBCT; Surgical guide design","lastPublishedDoi":"10.21203/rs.3.rs-8876559/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8876559/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003eTo develop an open‑source application composed of specialized modules for oral and dental implant surgery planning, built on the medical imaging platform 3D Slicer. The goal was to integrate a fragmented set of tools into a unified, reproducible, and computationally coherent workflow.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eSix Python scripted custom modules were developed and combined with three built‑in modules and five 3D Slicer extensions to form the Slicer–Free Surgical Planner (Slicer FSP). The system implements a complete digital workflow, from CBCT import and preprocessing to prosthetic simulation, implant planning, and surgical guide design. The architecture emphasizes modularity, interoperability, automated data handling, and standardized coordinate export to support downstream computational tasks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eCompared with using multiple independent 3D Slicer modules, Slicer FSP significantly reduced workflow complexity and overall working time. The integrated design introduced new technical capabilities, including automated slice and implant coordinate export, enhanced alignment tools, top‑down implant simulation, improved data management, and a bilingual interface. These features increased reproducibility, reduced user‑dependent variability, and enabled consistent execution of complex planning steps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eSlicer FSP is an open, extensible, and transparent platform for oral surgery and implant planning, offering a technically robust alternative to commercial systems. Its open‑source availability supports further development, validation, and integration into research pipelines, facilitating broader adoption of reproducible, modular, and community‑driven digital workflows.\u003c/p\u003e","manuscriptTitle":"Slicer FSP: An Open‑Source Modular Platform for Reproducible Dental Implant Surgery Planning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 06:55:18","doi":"10.21203/rs.3.rs-8876559/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":"2c56e547-6c2b-4f2e-a2bd-b9c66d13f63d","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62915956,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2026-02-18T14:41:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 06:55:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8876559","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8876559","identity":"rs-8876559","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}