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Table 1 Name and description of PET-AS methods used in this study, with references of published work using similar segmentation approaches

From: Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods

Algorithm

Description

Key references

AT

3D adaptive iterative thresholding, using background subtraction

Jentzen et al. [43], Drever et al. [44]

RG

3D region-growing with automatic seed finder and stopping criterion

Day et al. [45]

KM

3D K-mean iterative clustering with custom stopping criterion

Zaidi and El Naqa [8]

FCM

3D fuzzy C-mean iterative clustering with custom stopping criterion

Belhassen and Zaidi [46]

GCM

3D Gaussian mixture models based clustering with custom stopping criterion

Hatt et al. [37]

WT

Watershed transform-based algorithm, using sobel filter

Geets et al. [47], Tylski et al. [48]