Big Data in multiscale modelling: from medical image processing to personalized models

Tijana Geroski, Djordje Jakovljević, Nenad Filipović

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The healthcare industry is different from other industries–patient data are sensitive, their storage needs to be handled with care and in compliance with regulative, while prediction accuracy needs to be high. This fast expansion in medical image modalities and data collection leads to generation of so called “Big Data” which is time-consuming to be analyzed by medical experts. This paper provides an insight into the Big Data from the aspect of its role in multiscale modelling. Special attention is paid to the workflow, starting from medical image processing all the way to creation of personalized models and their analysis. A review of literature regarding Big Data in healthcare is provided and two proposed solutions are described–carotid artery ultrasound image processing and 3D reconstruction, and drug testing on personalized heart models. Related to the carotid artery ultrasound image processing, the starting point is ultrasound images, which are segmented using convolutional neural network U-net, while segmented masks were further used in 3D reconstruction of geometry. Related to the drug testing on personalized heart model, similar approach was proposed, images were used in creation of personalized 3D geometrical model that is used in computational modelling to determine pressure in the left ventricle before and after drug testing. All the aforementioned methodologies are complex, include Big Data analysis and should be performed using servers or high-performance computing. Future development of Big Data applications in healthcare domains offers a lot of potential due to new data standards, rapid development of research and technology, as well as strong government incentives.

Original languageEnglish
Article number72 (2023)
Number of pages22
JournalJournal of Big Data
Publication statusPublished - 22 May 2023

Bibliographical note

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit


The research was funded by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, contract number [451-03-47/2023-01/200107 (Faculty of Engineering, University of Kragujevac)]. This research
is also supported by the project that has received funding from the European Union’s Horizon 2020 research and innovation programmes under Grant agreements No 755320 (TAXINOMISIS project) and No 952603 (SGABU project).


  • Big Data
  • Multiscale modelling
  • Medical image processing
  • 3D reconstruction

ASJC Scopus subject areas

  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Information Systems and Management


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