Impact of consensus contours from multiple PET-segmentation methods on the accuracy of functionalvolume delineation

A. Schaefer (1) , Maximilien Vermandel (2, 3) , C. Baillet (3) , As Dewalle-Vignon (2) , R. Modzelewski (4) , P. Vera (4) , L. Massoptier (5) , C. Parcq (5) , D. Gibon (5) , T Fechter (6) , U. Nemer (6) , I. Gardin (4) , U. Nestle (6)

1 Department of Nuclear Medicine , Homburg, Germany
2 Université Lille Nord (France)
3 CHRU Lille - Centre Hospitalier Régional Universitaire [CHU Lille]
4 QuantIF-LITIS - Equipe Quantification en Imagerie Fonctionnelle
5 Aquilab
6 Department of Radiation Oncology, Freiburg, Germany

Abstract: Purpose: This study aimed to evaluate the impact of consensus algorithms on segmentation results when applied on clinical PET images. In particular, how majority vote or STAPLE algorithms could improve the final result in terms of accuracy and reproducibility when combining three semi-automatic segmentation algorithms. Methods: Three published approaches of segmentation (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 (NSCLC primary tumours, metastasis, lymphoma) were used to evaluate accuracy and reproducibility of the consensus approach in comparison with pathology ground truth or CT – ground truth surrogate. Results: Our results reflect the variable performance of individual segmentation algorithms for lesions of different tumour entities that is for PET images that differ in resolution, contrast and image noise. Independent on location and pathology of the lesion, however, the consensus method displays improved volume segmentation accuracy compared to the worst performing individual method in the majority of cases and is close to the best performing method in many cases. In addition, the implementation reveals high reproducibility of the segmentation results against small changes in the respective starting conditions. No significant difference between STAPLE and majority vote algorithms was found. Conclusion: This study shows that combining different PET-segmentation methods by application of a consensus algorithm offers robustness against the variable performance of individual segmentation methods and is therefore useful for radiation oncology purposes. It might also be relevant for other scenarios like the joining of expert recommendations in clinical routine and trials or the generation of multi-observer generated contours for standardisation of automatic contouring.

Mots clés: PET image segmentation; consensus algorithms; STAPLE; Radiation oncology

Domaines: Cancer

A. Schaefer, Maximilien Vermandel, C. Baillet, As Dewalle-Vignon, R. Modzelewski, et al.. Impact of consensus contours from multiple PET-segmentation methods on the accuracy of functionalvolume delineation. European Journal of Nuclear Medicine and Molecular Imaging, 2015. ⟨hal-01233427⟩