Search

18F-fluorodeoxyglucose positron emission tomography (18FDG PET) has become an essential technique in oncology. Accurate segmentation and uptake quantification are crucial in order to enable objective follow-up, the optimization of radiotherapy planning, and therapeutic evaluation. We have designed and evaluated a new, nearly automatic and operator-independent segmentation approach. This incorporated possibility theory, in order to take into account the uncertainty and inaccuracy inherent in the image. The approach remained independent of PET facilities since it did not require any preliminary calibration. Good results were obtained from phantom images [percent error =18.38% (mean) ± 9.72% (standard deviation)]. Results on simulated and anatomopathological data sets were quantified using different similarity measures and showed the method was efficient (simulated images: Dice index =82.18% ± 13.53% for SUV =2.5 ). The approach could, therefore, be an efficient and robust tool for uptake volume segmentation, and lead to new indicators for measuring volume of interest activity.

The aim of this study was to evaluate the impact of consensus algorithms on segmentation results when applied to clinical PET images. In particular, whether the use of the majority vote or STAPLE algorithm could improve the accuracy and reproducibility of the segmentation provided by the combination of three semiautomatic segmentation algorithms was investigated. Three published segmentation methods (contrast-oriented, possibility theory and adaptive thresholding) and two consensus algorithms (majority vote and STAPLE) were implemented in a single software platform (Artiview®). Four clinical datasets including different locations (thorax, breast, abdomen) or pathologies (primary NSCLC tumours, metastasis, lymphoma) were used to evaluate accuracy and reproducibility of the consensus approach in comparison with pathology as the ground truth or CT as a ground truth surrogate. Variability in the performance of the individual segmentation algorithms for lesions of different tumour entities reflected the variability in PET images in terms of resolution, contrast and noise. Independent of location and pathology of the lesion, however, the consensus method resulted in improved accuracy in volume segmentation compared with the worst-performing individual method in the majority of cases and was close to the best-performing method in many cases. In addition, the implementation revealed high reproducibility in the segmentation results with small changes in the respective starting conditions. There were no significant differences in the results with the STAPLE algorithm and the majority vote algorithm. This study showed that combining different PET segmentation methods by the use of a consensus algorithm offers robustness against the variable performance of individual segmentation methods and this approach would therefore be useful in radiation oncology. It might also be relevant for other scenarios such as the merging of expert recommendations in clinical routine and trials or the multiobserver generation of contours for standardization of automatic contouring.

Introduction [18F]-Fluorodeoxyglucose PET has become an essential technique in oncology. Accurate segmentation is important for treatment planning. With the increasing number of available methods, it will be useful to establish a reliable evaluation tool. Method Five methods for [18F]-fluorodeoxyglucose PET image segmentation (MIP-based, Fuzzy C-means, Daisne, Nestle and the 42% threshold-based approach) were evaluated on non-Hodgkin’s lymphoma lesions by comparing them with manual delineations performed by a panel of experts. The results were analyzed using different similarity measures. Intraoperator and interoperator variabilities were also studied. Results The maximum of intensity projection-based method provided results closest to the manual delineations set [binary Jaccard index mean (SD) 0.45 (0.15)]. The fuzzy C-means algorithm yielded slightly less satisfactory results. The application of a 42% threshold-based approach yielded results furthest from the manual delineations [binary Jaccard index mean (SD) 0.38 (0.16)]; the Daisne and the Nestle methods yielded intermediate results. Important intraoperator and interoperator variabilities were demonstrated. Conclusion A simple assessment framework based on comparisons with manual delineations was proposed. The use of a set of manual delineations performed by five different experts as the reference seemed to be suitable to take the intraoperator and the interoperator variabilities into account. The online distribution of the data set generated in this study will make it possible to evaluate any new segmentation method.

Fractal geometry may be an efficient tool for texture analysis in medical imaging. However its application is primarily restricted to 2D cases and at the only use of an approximation method of the fractal dimension (FD). Recently, multifractal analysis has showed interesting results in this field. This study focuses on the use of an optimized set of 3D fractal and multifractal features for the epileptogenic focus characterization in SPECT imaging. Our results showed that this optimized set, compared to various texture features, improved the classification rate by Support Vector Machines (SVM). Moreover, results were significantly better than the clinical method: SISCOM (Substraction Ictal SPECT Co-registred to MRI).

A new automatic method for tumor volume segmentation on PET images has been developed. The method introduced in this paper is based on previous works in MRA segmentation and involves both fuzzy set and possibility theories. Visual results prove the method efficiency which is confirmed by obtained Jaccard index.