Elsevier

Radiotherapy and Oncology

Volume 96, Issue 3, September 2010, Pages 302-307
Radiotherapy and Oncology

Review
Segmentation of positron emission tomography images: Some recommendations for target delineation in radiation oncology

https://doi.org/10.1016/j.radonc.2010.07.003Get rights and content

Abstract

Positron emission tomography can be used in radiation oncology for the delineation of target volumes in the treatment planning stage. Numerous publications deal with this topic and the scientific community has investigated many methodologies, ranging from simple uptake thresholding to very elaborate probabilistic models. Nevertheless, no consensus seems to emerge. This paper reviews delineation techniques that are popular in the literature. Special attention is paid to threshold-based techniques and the caveats of this methodology are pointed out by formal analysis. Next, a simple model of positron emission tomography is suggested in order to shed some light on the difficulties of target delineation and how they might be eventually overcome. Validation aspects are considered as well. Finally, a few recommendations are gathered in the conclusion.

Section snippets

From delineation to segmentation

The delineation of target volumes on PET images can obviously be done by hand with an appropriate computer interface. The major drawback of this approach is the variability of the resulting contours [15]. Because of their modest resolution [1], PET images look blurred and the human eye cannot easily distinguish the boundaries of the target. An additional difficulty is related to display settings (windowing level and width). Changing the colour scale or saturation can dramatically change the

Review of automatic delineation methodologies

Image segmentation can be achieved in various ways. The simplest method consists in considering each pixel independently and to determine its class label by looking solely at its value. This turns out to be equivalent to building the image histogram and to split it into several parts thanks to one or several thresholds. In a binary problem (e.g., with two classes: target and non-target), one threshold is sufficient.

Thresholding can be refined in several ways. The simplest and most common

Thresholding in question

As a matter of fact, thresholding is an old [41] but still very popular automatic method of segmenting PET images (see e.g., [15], [17], [18], [42], [43], [44], [45], [46], [47] and references therein). As the goal is to separate a region with high uptake from a background with lower uptake, the idea of an intensity threshold naturally emerges. It is both intuitive to understand and easy to implement: all pixels having an intensity that is lower than the threshold are labeled as non-target

Validation issues

The purpose of validation is basically to check that the considered delineation method is applicable to a broad range of cases with a reasonable accuracy. Quality and pertinence of validation thus depends on (i) the set of images it involves and (ii) the quality criteria it uses to assess the discrepancy between the obtained result and the desired one.

Several types of images can be used in validation. Computer-generated images are typically useful in the primary steps of validation, as a proof

Summary and conclusions

Accuracy of automatic target delineation is directly conditioned by image quality [61], [72]. Therefore, image acquisition and reconstruction are as important as the delineation technique itself. Increasing the acquisition duration or the tracer dose can contribute to improving the signal-to-noise ratio of the image (the larger amount of collected data reduces the statistical uncertainty in the reconstructed image). These requirements must naturally be put in the balance with patient comfort

Financial support

J.A.L. is a Research Associate with the Belgian fund of scientific research (Fonds National de la recherché scientifique, FRS-FNRS).

The authors have no financial relationship with the organizations that sponsored the research.

The authors have had full control of all primary data and agree to allow the journal to review their data if requested.

Acknowledgements

The author wishes to thank the reviewer for his/her useful comments and Anne Bol for her careful proofreading.

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