Journal article
Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211
M Hatt, JA Lee, CR Schmidtlein, I El Naqa, C Caldwell, E De Bernardi, W Lu, S Das, X Geets, V Gregoire, R Jeraj, MP MacManus, OR Mawlawi, U Nestle, AB Pugachev, H Schöder, T Shepherd, E Spezi, D Visvikis, H Zaidi Show all
Medical Physics | WILEY | Published : 2017
DOI: 10.1002/mp.12124
Abstract
Purpose: The purpose of this educational report is to provide an overview of the present stateof-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. Approach: A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets ..
View full abstractGrants
Awarded by National Cancer Institute
Funding Acknowledgements
We want to thank D. Nelson (MiM Software, Cleveland, OH), for his contribution as a consultant (industry observer) and the very helpful staff of the American Association of Physicists in Medicine for providing support to the task group. The work on this report was funded in part by the American Association of Physicists in Medicine and the contribution from few of us (CRS, HS and ASK) was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748.