[41] McClure, P., Rho, N., Lee, J.A., Kaczmarzyk, J.R., Zheng, C.Y., Ghosh, S.S., Nielson, D.M., Thomas, A.G., Bandettini, P., Pereira, F.: Knowing what you know in brain segmentation using bayesian deep neural networks. Frontiers in neuroinformatics (2019)
[42] Meilă, M.: Comparing clusterings—an information based distance. Journal of multivariate analysis (2007)
[43] Milnor, J.W., Spivak, M., Wells, R., Wells, R.: Morse theory (1963)
[44] Mnih, V.: Machine learning for aerial image labeling. University of Toronto (Canada) (2013)
[45] Mnih, V., Hinton, G.E.: Learning to detect roads in high-resolution aerial images. In: ECCV (2010)
[46] Mosinska, A., Marquez-Neila, P., Kozinski, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: CVPR (2018)
[47] Mou, L., Chen, L., Cheng, J., Gu, Z., Zhao, Y., Liu, J.: Dense dilated network with probability regularized walk for vessel detection. TMI (2019)
[48] Mou, L., Zhao, Y., Fu, H., Liu, Y., Cheng, J., Zheng, Y., Su, P., Yang, J., Chen, L., Frangi, A.F., et al.: Cs2-net: Deep learning segmentation of curvilinear structures in medical imaging. MedIA (2021)
[49] Murphy, K.P.: Conjugate bayesian analysis of the gaussian distribution. def (2σ2) (2007)
[50] Naeini, M.P., Cooper, G., Hauskrecht, M.: Obtaining well calibrated probabilities using bayesian binning. In: AAAI (2015)
[51] Nair, T., Precup, D., Arnold, D.L., Arbel, T.: Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. MedIA (2020)
[52] Papandreou, G., Yuille, A.L.: Perturb-and-map random fields: Using discrete optimization to learn and sample from energy models. In: ICCV (2011)
[53] Peng, H., Long, F., Zhao, T., Myers, E.: Proof-editing is the bottleneck of 3d neuron reconstruction: the problem and solutions. Neuroinformatics (2011)
[54] Rand, W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical association (1971)
[55] Ren, P., Xiao, Y., Chang, X., Huang, P.Y., Li, Z., Gupta, B.B., Chen, X., Wang, X.: A survey of deep active learning. ACM computing surveys (CSUR) (2021)
[56] Robins, V., Wood, P.J., Sheppard, A.P.: Theory and algorithms for constructing discrete Morse complexes from grayscale digital images. TPAMI (2011)
[57] Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI (2015)
[58] Rupprecht, C., Laina, I., DiPietro, R., Baust, M., Tombari, F., Navab, N., Hager, G.D.: Learning in an uncertain world: Representing ambiguity through multiple hypotheses. In: ICCV (2017)
[59] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks (2008)
[60] Seebock, P., Orlando, J.I., Schlegl, T., Waldstein, S.M., Bogunovic, H., Klimscha, S., Langs, G., Schmidt-Erfurth, U.: Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal oct. TMI (2019)
[61] Settles, B.: Active learning literature survey (2009)
[62] Shit, S., Paetzold, J.C., Sekuboyina, A., Ezhov, I., Unger, A., Zhylka, A., Pluim, J.P., Bauer, U., Menze, B.H.: cldice-a novel topology-preserving loss function for tubular structure segmentation. In: CVPR (2021)