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PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques

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Abstract

Historically, anatomical CT and MR images were used to delineate the gross tumour volumes (GTVs) for radiotherapy treatment planning. The capabilities offered by modern radiation therapy units and the widespread availability of combined PET/CT scanners stimulated the development of biological PET imaging-guided radiation therapy treatment planning with the aim to produce highly conformal radiation dose distribution to the tumour. One of the most difficult issues facing PET-based treatment planning is the accurate delineation of target regions from typical blurred and noisy functional images. The major problems encountered are image segmentation and imperfect system response function. Image segmentation is defined as the process of classifying the voxels of an image into a set of distinct classes. The difficulty in PET image segmentation is compounded by the low spatial resolution and high noise characteristics of PET images. Despite the difficulties and known limitations, several image segmentation approaches have been proposed and used in the clinical setting including thresholding, edge detection, region growing, clustering, stochastic models, deformable models, classifiers and several other approaches. A detailed description of the various approaches proposed in the literature is reviewed. Moreover, we also briefly discuss some important considerations and limitations of the widely used techniques to guide practitioners in the field of radiation oncology. The strategies followed for validation and comparative assessment of various PET segmentation approaches are described. Future opportunities and the current challenges facing the adoption of PET-guided delineation of target volumes and its role in basic and clinical research are also addressed.

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Notes

  1. The first-order derivative suppresses stretching and makes the contour behave like an elastic string. The second-order derivative suppresses bending and makes the model behave like a rigid rod.

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Acknowledgements

This work was supported by the Swiss National Science Foundation under grant SNSF 3152A0-102143 and the National Institutes of Health under grant 1K25CA128809-01A1.

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  1. Division of Nuclear Medicine, Geneva University Hospital, 1211, Geneva 4, Switzerland

    Habib Zaidi

  2. Geneva Neuroscience Center, Geneva University, 1205, Geneva, Switzerland

    Habib Zaidi

  3. Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, 63110, USA

    Issam El Naqa

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Zaidi, H., El Naqa, I. PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur J Nucl Med Mol Imaging 37, 2165–2187 (2010). https://doi.org/10.1007/s00259-010-1423-3

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