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Radiation Oncology
Published in: Radiation Oncology 1/2022

Open Access 01-12-2022 | Radiation Treatment Planning | Research

A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation

Authors: Sebastian Marschner, Manasi Datar, Aurélie Gaasch, Zhoubing Xu, Sasa Grbic, Guillaume Chabin, Bernhard Geiger, Julian Rosenman, Stefanie Corradini, Maximilian Niyazi, Tobias Heimann, Christian Möhler, Fernando Vega, Claus Belka, Christian Thieke

Published in: Radiation Oncology | Issue 1/2022

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Abstract

Background

We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning.

Methods

The algorithm identifies the region of interest (ROI) automatically by detecting anatomical landmarks around the specific organs using a deep reinforcement learning technique. The segmentation is restricted to this ROI and performed by a deep image-to-image network (DI2IN) based on a convolutional encoder-decoder architecture combined with multi-level feature concatenation. The algorithm is commercially available in the medical products “syngo.via RT Image Suite VB50” and “AI-Rad Companion Organs RT VA20” (Siemens Healthineers). For evaluation, thoracic CT images of 237 patients and pelvic CT images of 102 patients were manually contoured following the Radiation Therapy Oncology Group (RTOG) guidelines and compared to the DI2IN results using metrics for volume, overlap and distance, e.g., Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD95). The contours were also compared visually slice by slice.

Results

We observed high correlations between automatic and manual contours. The best results were obtained for the lungs (DSC 0.97, HD95 2.7 mm/2.9 mm for left/right lung), followed by heart (DSC 0.92, HD95 4.4 mm), bladder (DSC 0.88, HD95 6.7 mm) and rectum (DSC 0.79, HD95 10.8 mm). Visual inspection showed excellent agreements with some exceptions for heart and rectum.

Conclusions

The DI2IN algorithm automatically generated contours for organs at risk close to those by a human expert, making the contouring step in radiation treatment planning simpler and faster. Few cases still required manual corrections, mainly for heart and rectum.
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Metadata
Title
A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation
Authors
Sebastian Marschner
Manasi Datar
Aurélie Gaasch
Zhoubing Xu
Sasa Grbic
Guillaume Chabin
Bernhard Geiger
Julian Rosenman
Stefanie Corradini
Maximilian Niyazi
Tobias Heimann
Christian Möhler
Fernando Vega
Claus Belka
Christian Thieke
Publication date
01-12-2022
Publisher
BioMed Central
Published in
Radiation Oncology / Issue 1/2022
Electronic ISSN: 1748-717X
DOI
https://doi.org/10.1186/s13014-022-02102-6

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