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Strahlentherapie und Onkologie
Published in: Strahlentherapie und Onkologie 12/2019

Open Access 01-12-2019 | Computed Tomography | Original Article

Automatic image segmentation based on synthetic tissue model for delineating organs at risk in spinal metastasis treatment planning

Authors: Olaf Wittenstein, Patrick Hiepe, Lars Henrik Sowa, Elias Karsten, Iris Fandrich, Juergen Dunst

Published in: Strahlentherapie und Onkologie | Issue 12/2019

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Abstract

Purpose

One of the main goals in software solutions for treatment planning is to automatize delineation of organs at risk (OARs). In this pilot feasibility study a clinical validation was made of computed tomography (CT)-based extracranial auto-segmentation (AS) using the Brainlab Anatomical Mapping tool (AM).

Methods

The delineation of nine extracranial OARs (lungs, kidneys, trachea, heart, liver, spinal cord, esophagus) from clinical datasets of 24 treated patients was retrospectively evaluated. Manual delineation of OARs was conducted in clinical routine and compared with AS datasets using AM. The Dice similarity coefficient (DSC) and maximum Hausdorff distance (HD) were used as statistical and geometrical measurements, respectively. Additionally, all AS structures were validated using a subjective qualitative scoring system.

Results

All patient datasets investigated were successfully processed with the evaluated AS software. For the left lung (0.97 ± 0.03), right lung (0.97 ± 0.05), left kidney (0.91 ± 0.07), and trachea (0.93 ± 0.04), the DSC was high with low variability. The DSC scores of other organs (right kidney, heart, liver, spinal cord), except the esophagus, ranged between 0.7 and 0.9. The calculated HD values yielded comparable results. Qualitative assessment showed a general acceptance in more than 85% of AS OARs—except for the esophagus.

Conclusions

The Brainlab AM software is ready for clinical use in most of the OARs evaluated in the thoracic and abdominal region. The software generates highly conformal structure sets compared to manual contouring. The current study design needs revision for further research.
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Metadata
Title
Automatic image segmentation based on synthetic tissue model for delineating organs at risk in spinal metastasis treatment planning
Authors
Olaf Wittenstein
Patrick Hiepe
Lars Henrik Sowa
Elias Karsten
Iris Fandrich
Juergen Dunst
Publication date
01-12-2019
Publisher
Springer Berlin Heidelberg
Published in
Strahlentherapie und Onkologie / Issue 12/2019
Print ISSN: 0179-7158
Electronic ISSN: 1439-099X
DOI
https://doi.org/10.1007/s00066-019-01463-4

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