VarVis

Visualizing Anatomical Variation in Branching Structures

Conference Paper (2016)
Author(s)

Noeska Natasja Smit (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Annelot Kraima (Leiden University Medical Center)

Daniel Jansma (Leiden University Medical Center)

Marco DeRuiter (Leiden University Medical Center)

Elmar Eisemann (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Anna Vilanova Bartroli (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Research Group
Computer Graphics and Visualisation
URL related publication
http://graphics.tudelft.nl/Publications-new/2016/SKJDEV16
More Info
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Publication Year
2016
Language
English
Research Group
Computer Graphics and Visualisation
Pages (from-to)
1-5
Publisher
Eurographics
ISBN (print)
978-3-03868-014-7
Event
EuroVis 2016 (2016-06-06 - 2016-06-10), Groningen, Netherlands
Downloads counter
204

Abstract

Anatomical variations are naturally-occurring deviations from typical human anatomy. While these variations are considered normal and non-pathological, they are still of interest in clinical practice for medical specialists such as radiologists and transplantation surgeons. The complex variations in branching structures, for instance in arteries or nerves, are currently visualized side-by-side in illustrations or expressed using plain text in medical publications. In this work, we present a novel way of visualizing anatomical variations in complex branching structures for educational purposes: VarVis. VarVis consists of several linked views that reveal global and local similarities and differences in the variations. We propose a novel graph representation to provide an overview of the topological changes. Our solution involves a topological
similarity measure, which allows the user to select variations at a global level based on their degree of similarity. After a selection is made, local topological differences can be interactively explored using illustrations and topology graphs. We also incorporate additional information regarding the probability of the various cases. Our solution has several advantages over traditional approaches, which we demonstrate in an evaluation.