Dicoogle Framework for Medical Imaging Teaching and Research

EasyChair Preprint 3768

Abstract

One of the most noticeable trends in healthcare over the last years is the continuous growth of data volume produced and its heterogeneity. In the medical imaging field, the evolution of digital systems is supported by the PACS concept and the DICOM standard. These technologies are deeply grounded in medical laboratories, supporting the production and providing healthcare practitioners with the ability to set up collaborative work environments with researchers and academia to study and improve healthcare practice. However, the complexity of those systems and protocols makes difficult and time-consuming to prototype new ideas or develop applied research, even for skilled users with training in those environments. Dicoogle emerges as a reference tool to achieve those objectives through a set of resources aggregated in the form of a learning pack. It is an open-source PACS archive that, on the one hand, provides a comprehensive view of the PACS and DICOM technologies and, on the other hand, provides the user with tools to easily expand its core functionalities. This paper describes the Dicoogle framework, with particular emphasis in its Learning Pack package, the resources available and the impact of the platform in research and academia. It starts by presenting an overview of its architectural concept, the most recent research backed up by Dicoogle, some remarks obtained from its use in teaching, and worldwide usage statistics of the software. Moreover, a comparison between the Dicoogle platform and the most popular open-source PACS in the market is presented.

Keyphrases: DICOM, Dicoogle, Learning pack, Medical Imaging, PACS, teaching

@booklet{EasyChair:3768,
  author    = {Rui Lebre and Eduardo Pinho and Jorge Miguel Silva and Carlos Costa},
  title     = {Dicoogle Framework for Medical Imaging Teaching and Research},
  howpublished = {EasyChair Preprint 3768},
  year      = {EasyChair, 2020}}