Paper
21 March 2014 Interactive approach to segment organs at risk in radiotherapy treatment planning
Jose Dolz, Hortense A. Kirisli, Romain Viard, Laurent Massoptier
Author Affiliations +
Abstract
Accurate delineation of organs at risk (OAR) is required for radiation treatment planning (RTP). However, it is a very time consuming and tedious task. The use in clinic of image guided radiation therapy (IGRT) becomes more and more popular, thus increasing the need of (semi-)automatic methods for delineation of the OAR. In this work, an interactive segmentation approach to delineate OAR is proposed and validated. The method is based on the combination of watershed transformation, which groups small areas of similar intensities in homogeneous labels, and graph cuts approach, which uses these labels to create the graph. Segmentation information can be added in any view – axial, sagittal or coronal -, making the interaction with the algorithm easy and fast. Subsequently, this information is propagated within the whole volume, providing a spatially coherent result. Manual delineations made by experts of 6 OAR - lungs, kidneys, liver, spleen, heart and aorta – over a set of 9 computed tomography (CT) scans were used as reference standard to validate the proposed approach. With a maximum of 4 interactions, a Dice similarity coefficient (DSC) higher than 0.87 was obtained, which demonstrates that, with the proposed segmentation approach, only few interactions are required to achieve similar results as the ones obtained manually. The integration of this method in the RTP process may save a considerable amount of time, and reduce the annotation complexity.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jose Dolz, Hortense A. Kirisli, Romain Viard, and Laurent Massoptier "Interactive approach to segment organs at risk in radiotherapy treatment planning", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90343S (21 March 2014); https://doi.org/10.1117/12.2042766
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Cited by 4 scholarly publications.
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KEYWORDS
Image segmentation

Liver

Radiotherapy

Computed tomography

Heart

Image processing algorithms and systems

Kidney

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