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

Abstract

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.

Keywords:

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

View correction statement:

Correction

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that form the basis of this project were obtained from medical databases Open Access Series of Brain Imaging Studies, OASIS (2020), (http://oasis-brains.org) for healthy brains and Alzheimer’s Disease Neuroimaging Initiative, ADNI (2020), (http://adni.loni.usc.edu/) for the brain affected by Alzheimer’s Disease. Patient MRI datasets have been anonymised.

Correction Statement

This article was originally published with errors, which have now been corrected in the online version. Please see Correction (http://dx.doi.org/10.1080/17453054.2022.2069714)

Additional information

Funding

This work was supported by the Welcome Institutional Strategic Support Fund awarded to the University of Dundee under Grant 204816/Z/16/Z.