Abstract
Abstract Three-dimensional visualizations and 3D-printed organs are used increasingly for teaching, surgery planning, patient education, and interventions. Hence, pipelines for the creation of the necessary geometric data from CT or MR images on a per-patient basis are needed. Furthermore, modern 3D printing techniques enable new possibilities for the models with regard to color, softness, and textures. However, to utilize these new features, the respective information has to be derived from the medical images in addition to the geometry of the relevant organ structures. In this work, we propose an automatable pipeline for the creation of realistic, patientspecific 3D-models for visualization and 3D printing in the context of liver surgery and discuss remaining challenges.
Highlights
Medical images resulting from Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) play an important role for clinical diagnosis and therapy decisions
The presented pipeline was created in the context of the VIVATOP project [2] and focuses on the creation of models in the scope of liver surgery planning and training, but its principles can be translated to other applications as well
We proposed an end-to-end pipeline for the generation of highly realistic virtual and physical organ models from standard CT and MRI data, using novel approaches for 3D printing and tissue characterization among others
Summary
Medical images resulting from Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) play an important role for clinical diagnosis and therapy decisions. Modern visualization technologies, including virtual and augmented reality as well as recent 3D printing methods, enable new ways of exploring such images in the context of medical training, patient education and surgery planning [1]. Using modern 3D printing technologies, all these aspects can be transferred into highly realistic organ models, which have the potential to revolutionize operation planning, student education and patient communication. The processing of the patient data involves multiple interdependent steps forming a dedicated workflow. While such a process chain may be feasible in a manual way for training models, automation of a higher degree is desirable for scenarios that involve patient-specific models. The presented pipeline was created in the context of the VIVATOP project [2] and focuses on the creation of models in the scope of liver surgery planning and training, but its principles can be translated to other applications as well
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