Molecular Imaging 2017

Report of AAPM TG211: Classification and evaluation strategies of auto-segmentation approaches for PET M. Hatt 1 , J. Lee 2 , C. Caldwell 3 , I. El Naqa 4 , C.R. Schmidtlein 5 , E. De Bernardi 6 , W. Lu 7 , U. Nestle 8 , D. Visvikis 1 , T. Shepherd 9 , S. Das 10 , O. Mawlawi 11 , V. Gregoire 2 , H. Schöder 5 , R. Jeraj 12 , A. Pugachev, E. Spezi, M. MacManus 13 , X. Geets 2 , H. Zaidi 14 , A.S. Kirov 5* Conclusions: Based on the large number of published PET-AS algorithms and their relative lack of validation, selecting and implementing one algorithm among those available is challenging. There is however accumulating evidence in available comparison studies that PET-AS algorithms relying on advanced image paradigms perform better than simple threshold-based approaches. The second conclusion of this report is that a standard test (i.e. a benchmark) dedicated to evaluation of both existing and future PET-AS algorithms needs to be designed. The first steps in designing this standard are presented in the second half of the report. The primary intention of this benchmark is to aid clinicians in evaluating and selecting PET-AS algorithms for use in clinical practice.

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