B. Foster, U. Bagci, A. Mansoor, Z. Xu, and D. Mollura, A review on segmentation of positron emission tomography images, Comput Biol Med, vol.50, pp.76-96, 2014.

J. F. Daisne, M. Sibomana, A. Bol, T. Doumont, M. Lonneux et al., Tri-dimensional automatic segmentation of PET volumes based on measured source-tobackground ratios: influence of reconstruction algorithms, Radiother Oncol, vol.69, pp.247-250, 2003.

U. Nestle, A. Schaefer-schuler, S. Kremp, A. Groeschel, D. Hellwig et al., Target volume definition for 18F-FDG PET-positive lymph nodes in radiotherapy of patients with non-small cell lung cancer, Eur J Nucl Med Mol I, vol.34, pp.453-462, 2007.

L. Bi, J. Kim, D. Feng, and M. Fulham, Multi-stage thresholded region classification for whole-body PET-CT lymphoma studies, pp.569-576, 2014.

J. Cheng-liao and J. Qi, Segmentation of mouse dynamic PET images using a multiphase level set method, Phys Med Biol, vol.55, pp.6549-6569, 2010.

M. Wanet, A. Lee, B. Weynand, M. De-bast, A. Pncelet et al., Gradientbased delineation of the primary GTV on FDG-PET in non-small cell lung cancer: A comparison with thresholdbased approaches, CT and surgical specimens, Radiother Oncol, vol.98, pp.117-125, 2011.

M. Hatt, C. Cheze-le-rest, P. Descourt, A. Dekker, D. De-ruysscher et al., Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications, Int J Radiat Oncol, vol.77, pp.301-308, 2010.
URL : https://hal.archives-ouvertes.fr/inserm-00537776

S. Belhassen and H. Zaidi, A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET, Med Phys, vol.37, pp.1309-1324, 2010.

U. Bagci, J. Udupa, N. Mendhiratta, B. Foster, Z. Xu et al., Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images, Med Image Anal, vol.17, pp.929-945, 2013.

P. Tylski, G. Bonniaud, E. Decencière, J. Stawiaski, J. Coulot et al., 18 F-FDG PET images segmentation using morphological watershed: A phantom study, vol.4, pp.2063-2067, 2006.

. Grossiordé, H. Talbot, N. Passat, M. Meignan, P. Tervé et al., Hierarchies and shape-space for PET image segmentation, pp.1118-1121, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01169944

A. Dewalle-vignion, N. Betrouni, R. Lopes, D. Huglo, S. Stute et al., A new method for volume segmentation of PET images, based on possibility theory, IEEE T Med Imaging, vol.30, pp.409-423, 2011.

E. Grossiord, H. Talbot, N. Passat, M. Meignan, and L. Najman, Automated 3D lymphoma lesion segmentation from PET/CT characteristics, pp.174-178, 2107.
URL : https://hal.archives-ouvertes.fr/hal-01616459

W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva et al., Random walk and graph cut for co-segmentation of lung tumor on PET-CT images, IEEE T Image Process, vol.24, pp.5854-5867, 2015.

C. Lartizien, M. Rogez, A. Susser, F. Giammarile, E. Niaf et al., Computer aided staging of lymphoma patients with FDG PET/CT imaging based on textural information, vol.ISBI, pp.118-121, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01050890

P. Ahmadvand, N. Duggan, F. Bénard, and G. Hamarneh, Tumor lesion segmentation from 3D PET using a machine learning driven active surface, MLMI Workshop, pp.271-278, 2016.

V. Machairas, T. Baldeweck, T. Walter, and E. Decencière, Hierarchical multi-scale supervoxel matching using random forest for automatic semi-dense abdominal image registration, pp.1409-1413, 2017.

P. Conze, V. Noblet, F. Rousseau, F. Heitz, V. Blasi et al., Scale-adaptive supervoxelbased random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans, Int J Comput Ass Rad, vol.12, pp.223-233, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01573601

P. Salembier, A. Oliveras, and L. Garrido, Anti-extensive connected operators for image and sequence processing, IEEE T Image Process, vol.7, pp.555-570, 1998.

P. Monasse and F. Guichard, Fast computation of a contrast invariant image representation, IEEE T Image Process, vol.9, pp.860-872, 2000.

J. Matas, O. Chum, M. Urban, and T. Pajdla, Robust wide baseline stereo from maximally stable extremal regions, pp.384-396, 2002.

F. Alvarez, . Grossiordé, B. Romaniuk, B. Naegel, C. Kurtz et al., Multicriteria 3D PET image segmentation. IPTA, pp.346-351, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01616446

E. Carlinet and T. Géraud, Comparative review of component-tree computation algorithms, IEEE T Image Process, vol.23, pp.3885-3895, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01474830

L. Grady, Random walks for image segmentation, IEEE T Pattern Anal, vol.28, pp.1768-1783, 2006.