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Related Experiment Video

Updated: May 20, 2025

3D Printing of Preclinical X-ray Computed Tomographic Data Sets
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Protocol for Converting DICOM Files to STL Models Using 3D Slicer and Ultimaker Cura.

Malena Pérez-Sevilla1, Fernando Rivas-Navazo2, Pedro Latorre-Carmona3

  • 1Department of Digitalization, Universidad de Burgos, Avda. Cantabria sn, 09006 Burgos, Spain.

Journal of Personalized Medicine
|March 26, 2025
PubMed
Summary
This summary is machine-generated.

This study outlines a protocol for converting medical DICOM images into 3D printable models using open-source software. The method ensures accurate anatomical replication for applications in personalized medicine and surgical planning.

Keywords:
3D Slicer and Ultimaker CuraDICOM-to-STL conversionRapid Prototyping in Medicinemedical image processingsurgical planningthree-dimensional anatomical modelling

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Area of Science:

  • Medical Imaging and 3D Printing
  • Biomedical Engineering
  • Open-Source Software Applications in Medicine

Background:

  • 3D printing is crucial for surgical planning and medical education.
  • Converting medical imaging data into 3D models is essential for creating patient-specific anatomical replicas.

Purpose of the Study:

  • To present a comprehensive protocol for converting DICOM files into 3D printable GCODE files.
  • To develop an optimized workflow for accurate and efficient 3D anatomical model generation.

Main Methods:

  • Utilized open-source "3D Slicer" for DICOM to STL conversion and segmentation.
  • Employed "Ultimaker Cura" for generating GCODE files for 3D printing.
  • Validated the protocol using deviation analysis for structural fidelity and morphological accuracy.

Main Results:

  • Achieved high fidelity in anatomical structures with narrow deviation ranges.
  • Generated accurate and reproducible 3D anatomical models from DICOM data.
  • Demonstrated the protocol's adaptability to various 3D printers and materials.

Conclusions:

  • The protocol offers an effective, accessible, and accurate method for creating 3D anatomical models.
  • This open-source, low-cost approach democratizes 3D modeling in medical sciences and education.
  • Future work may involve patient data integration and benchmarking against alternative methods.