3D Printing from Radiology Images: DICOM Segmentation Comparison

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Three Dimensional (3D) printing has emerged as a disruptive technology in healthcare and created a new channel for delivering personalized care to patients. 3D printing is now being leveraged to create personalized medical devices and surgical instruments, plan complex procedures, and provide training to future medical professionals. As the accessibility of 3D printing technology increases, more healthcare organizations are adopting programs to reduce model cost and lead times, while investigating new clinical applications. Although there is tremendous potential for medical 3D printing, significant barriers exist to its widespread adoption, mainly in the form of technical challenges in the processing of medical imaging data and project start-up cost.


  • Edward Stefanowicz, MBA, RT(R)(MR)
    Neuroscience Institute, Geisinger Health System
  • Sarah Flora RT(R)(MR)
    Department of Radiology, Geisinger Health System
  • Kevin Anton, MD, PHD
    Vascular and Interventional Radiology, Thomas Jefferson University Hospitals 
    Former affiliation: Department of Radiology, Geisinger Health System
  • Bill O’Connel
    Materialise USA
  • Todd Pietila, BSc, MBA
    Materialise USA
  • Aalpen A. Patel, Chair of Department Radiology, Vice Chair of Informatics (Radiology), Medical Director of 3D Lab
    Department of Radiology, Geisinger Health System

In this study, we investigate two common methods of preparing 3D printable files from Digital Imaging and Communications in Medicine (DICOM) images to compare software effectiveness and efficiency. While both methods are capable of producing results that meet the needs of referring physicians and patients, the optimized workflow demonstrated by the Mimics Innovation Suite allows for faster processing due to dedicated software features.

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