The transparent minds: methods of creation of 3D digital models from patient specific data

Hana Pokojna (Lead / Corresponding author), Caroline Erolin, Christopher Henstridge

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This paper focuses on the method for creating 3-dimensional (3D) digital models extracted from patient- specific scans of the brain. The described approach consists of several cross-platform stages: raw data segmentation, data correction in 3D-modelling software, post-processing of the 3D digital models and their presentation on an interactive web-based platform. This method of data presentation offers a cost and time effective option to present medical data accurately. An important aspect of the process is using real patient data and enriching the traditional slice-based representation of the scans with 3D models that can provide better understanding of the organs' structures. The resulting 3D digital models also form the basis for further processing into different modalities, for example models in Virtual Reality or 3D physical model printouts. The option to make medical data less abstract and more understandable can extend their use beyond diagnosis and into a potential aid in anatomy and patient education. The methods presented in this paper were originally based on the master thesis 'Transparent Minds: Testing for Efficiency of Transparency in 3D Physical and 3D Digital Models', which focussed on creating and comparing the efficiency of transparent 3D physical and 3D digital models from real-patient data.

Original languageEnglish
Pages (from-to)17-31
Number of pages15
JournalJournal of Visual Communication in Medicine
Issue number2
Early online date12 Jan 2022
Publication statusPublished - 3 Apr 2022


  • 3D models
  • Alzheimer’s disease
  • data segmentation
  • medical art
  • medical visualization
  • patient data

ASJC Scopus subject areas

  • Health Professions (miscellaneous)
  • Visual Arts and Performing Arts


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