Principles of Visualization in Radiation Oncology

Matthias Schlachter, Bernhard Preim, Katja Bühler, Renata Raidou
Principles of Visualization in Radiation Oncology
Oncology and Informatics, 1:1-11, January 2020.

Information

Abstract

Background: Medical visualization employs elements from computer graphics to create meaningful, interactive visual representations of medical data, and it has become an influential field of research for many advanced applications like radiation oncology, among others. Visual representations employ the user’s cognitive capabilities to support and accelerate diagnostic, planning, and quality assurance workflows based on involved patient data. Summary: This article discusses the basic underlying principles of visualization in the application domain of radiation oncology. The main visualization strategies, such as slice-based representations and surface and volume rendering are presented. Interaction topics, i.e., the combination of visualization and automated analysis methods, are also discussed. Key Messages: Slice-based representations are a common approach in radiation oncology, while volume visualization also has a long-standing history in the field. Perception within both representations can benefit further from advanced approaches, such as image fusion and multivolume or hybrid rendering. While traditional slice-based and volume representations keep evolving, the dimensionality and complexity of medical data are also increasing. To address this, visual analytics strategies are valuable, particularly for cohort or uncertainty visualization. Interactive visual analytics approaches represent a new opportunity to integrate knowledgeable experts and their cognitive abilities in exploratory processes which cannot be conducted by solely automatized methods.

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BibTeX

@article{raidou_2020Onc,
  title =      "Principles of Visualization in Radiation Oncology",
  author =     "Matthias Schlachter and Bernhard Preim and Katja B\"{u}hler
               and Renata Raidou",
  year =       "2020",
  abstract =   "Background: Medical visualization employs elements from
               computer graphics to create meaningful, interactive visual
               representations of medical data, and it has become an
               influential field of research for many advanced applications
               like radiation oncology, among others. Visual
               representations employ the user’s cognitive capabilities
               to support and accelerate diagnostic, planning, and quality
               assurance workflows based on involved patient data. Summary:
               This article discusses the basic underlying principles of
               visualization in the application domain of radiation
               oncology. The main visualization strategies, such as
               slice-based representations and surface and volume rendering
               are presented. Interaction topics, i.e., the combination of
               visualization and automated analysis methods, are also
               discussed. Key Messages: Slice-based representations are a
               common approach in radiation oncology, while volume
               visualization also has a long-standing history in the field.
               Perception within both representations can benefit further
               from advanced approaches, such as image fusion and
               multivolume or hybrid rendering. While traditional
               slice-based and volume representations keep evolving, the
               dimensionality and complexity of medical data are also
               increasing. To address this, visual analytics strategies are
               valuable, particularly for cohort or uncertainty
               visualization. Interactive visual analytics approaches
               represent a new opportunity to integrate knowledgeable
               experts and their cognitive abilities in exploratory
               processes which cannot be conducted by solely automatized
               methods.",
  month =      jan,
  doi =        "https://doi.org/10.1159/000504940",
  journal =    "Oncology and Informatics",
  volume =     "1",
  pages =      "1--11",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_2020Onc/",
}