SPEQTACLE: A new fuzzy clustering algorithm for fully automatic delineation of tumors in PET images

Accurate and robust tumor delineation using PET images is crucial for the extraction of features in therapy response monitoring and radiotherapy target definition. Manual delineation suffers from high inter- and intra-operator variability. Amongst the methods previously proposed, fuzzy c-means (FCM)...

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Veröffentlicht in:The Journal of nuclear medicine (1978) 2014-05, Vol.55
Hauptverfasser: Lapuyade-Lahorgue, Jérôme, Visvikis, Dimitris, Hatt, Mathieu
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Sprache:eng
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Zusammenfassung:Accurate and robust tumor delineation using PET images is crucial for the extraction of features in therapy response monitoring and radiotherapy target definition. Manual delineation suffers from high inter- and intra-operator variability. Amongst the methods previously proposed, fuzzy c-means (FCM) has been popular due to its flexibility and low computational complexity. The objective of this work was to develop a modified FCM approach to handle PET images specific spatial resolution and noise properties.SPEQTACLE (Spatial Positron Emission image Quantification of Tumor-AutomatiC Lp-norm Estimation) is based on FCM in which the Euclidian norm is replaced by a more appropriate one on a case-by-case basis. More specifically an automated scheme was developed to estimate the optimal norm, as well as the number of clusters for each image, with the user input reduced to locating the tumor. SPEQTACLE was compared to the standard FCM (FCM1) and the kernel-regularized FCM (FCM2), as well as the Fuzzy Locally Adaptive Bayesian (FLAB) as a state-of-the-art method. The evaluation was carried out on a database of simulated and real clinical PET images with a wide range of tumor sizes and shapes, contrast, noise level, voxel sizes and intra-tumor tracer heterogeneity.In terms of accuracy, SPEQTACLE consistently outperformed other FCM implementations (errors 9.8±5.3% vs. 28±14% and 20±15% for FCM1 and FCM2 respectively) as well as FLAB in the majority of cases (15.6%±13.1%). Improved robustness and reproducibility of the algorithm was also achieved thanks to its ability to automatically estimate the optimal parameters on an image-by-image basis without compromising computational time and algorithm convergence.SPEQTACLE outperformed state-of-the-art methods, especially in challenging cases of low contrast, high noise, complex shapes and high intra-tumor heterogeneity. It is fully automatic and computationally efficient. Finally SPEQTACLE may be easily extended to multimodal PET/CT/MRI analysis which will be investigated in the future.
ISSN:0161-5505
1535-5667