C. Archambeau and M. Verleysen, Robust Bayesian clustering, Neural Networks, vol.20, issue.1, pp.129-138, 2007.

O. Bernard and A. Lalande, Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?, IEEE Trans. on Medical Imaging, vol.37, issue.11, pp.2514-2525, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01803621

A. Jakab, Segmenting Brain Tumors with the Slicer 3D Software, vol.16, 2012.

T. Kohlberger and V. Singh, Evaluating segmentation error without ground truth, MICCAI 2012, pp.528-536, 2012.

D. J. Mackay, Bayesian interpolation, Neural Computation, vol.4, pp.415-447, 1991.

B. H. Menze and A. Jakab, The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Trans. on Medical Imaging, vol.34, issue.10, pp.1993-2024, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00935640

R. Robinson and O. Oktay, Real-time prediction of segmentation quality, MICCAI 2018 -21st International Conference, pp.578-585, 2018.

R. Robinson and V. V. Valindria, Automated quality control in image segmentation: application to the UK biobank cardiovascular magnetic resonance imaging study, Journal of Cardiovascular Magnetic Resonance, vol.21, issue.1, 2019.

A. G. Roy and S. Conjeti, Bayesian quicknat: Model uncertainty in deep wholebrain segmentation for structure-wise quality control, NeuroImage, vol.195, pp.11-22, 2019.

V. V. Valindria and I. Lavdas, Reverse classification accuracy: Predicting segmentation performance in the absence of ground truth, IEEE Trans. on Medical Imaging, vol.36, issue.8, pp.1597-1606, 2017.

Y. Xu and D. S. Berman, Automated Quality Control for Segmentation of Myocardial Perfusion SPECT, Journal of Nuclear Medicine, vol.50, issue.9, pp.1418-1426, 2009.