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Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation.
Uncertainty evaluation metric for brain tumour segmentation
Mehta, R., Filos, A., Gal, Y., & Arbel, T. (2020) · 2005
Earlier work this paper cites.
Bi-rads lexicon for us and mammography: interobserver variability and positive predictive value
Lazarus, E., Mainiero, M. B., Schepps, B., Koelliker, S. L., & Livingston, L. S. (2006) · 2006
Earlier work this paper cites.
Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (simple)
Langerak, T. R., van der Heide, U. A., Kotte, A. N., Viergever, M. A., Van Vulpen, M., & Pluim, J. P. (2010) · 2008
Earlier work this paper cites.
3d segmentation in the clinic: A grand challenge ii: Ms lesion segmentation
Styner, M., Lee, J., Chin, B., Chin, M., Commowick, O., Tran, H., Markovic-Plese, S., Jewells, V., & Warfield, S. (2008) · 2008
Earlier work this paper cites.
A generative model for image segmentation based on label fusion
Sabuncu, M. R., Yeo, B. T., Van Leemput, K., Fischl, B., & Golland, P. (2010) · 2010
Earlier work this paper cites.
The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A. et al. (2011) · 2011
Earlier work this paper cites.
A pattern recognition approach to zonal segmentation of the prostate on mri
Litjens, G., Debats, O., van de Ven, W., Karssemeijer, N., & Huisman, H. (2012) · 2012
Earlier work this paper cites.
Interobserver variability in the ct assessment of honeycombing in the lungs
Watadani, T., Sakai, F., Johkoh, T., Noma, S., Akira, M., Fujimoto, K., Bankier, A. A., Lee, K. S., Müller, N. L., Song, J.-W. et al. (2013) · 2013
Earlier work this paper cites.
Sampling image segmentations for uncertainty quantification
Lê, M., Unkelbach, J., Ayache, N., & Delingette, H. (2016) · 2016
Earlier work this paper cites.
Uncertainty estimates and multi-hypotheses networks for optical flow
Ilg, E., Cicek, O., Galesso, S., Klein, A., Makansi, O., Hutter, F., & Brox, T. (2018) · 2018
Cited alongside, same era.
No new-net
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2019) · 2018
Cited alongside, same era.
A probabilistic u-net for segmentation of ambiguous images
Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., Eslami, S., Jimenez Rezende, D., & Ronneberger, O. (2018) · 2018
Cited alongside, same era.
Ensembles of densely-connected cnns with label-uncertainty for brain tumor segmentation
McKinley, R., Meier, R., & Wiest, R. (2019) · 2018
Cited alongside, same era.
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., Prastawa, M., Alberts, E., Lipkova, J., Freymann, J., Kirby, J., Bilello, M., Fathallah-Shaykh, H., Wiest, R., Kirschke, J., Wiestler, B., Colen, R., Kotrotsou, A., Lamontagne, P., Marcus, D., Milchenko, M., Nazeri, A., Weber, M.-A., Mahajan, A., Baid, U., Gerstner, E., Kwon, D., Acharya, G., Agarwal, M., Alam, M., Albiol, A., Albiol, A., Albiol, F. J., Alex, V., Allinson, N., Amorim, P. H. A., Amrutkar, A., Anand, G., Andermatt, S., Arbel, T., Arbelaez, P., Avery, A., Azmat, M., B., P., Bai, W., Banerjee, S., Barth, B., Batchelder, T., Batmanghelich, K., Battistella, E., Beers, A., Belyaev, M., Bendszus, M., Benson, E., Bernal, J., Bharath, H. N., Biros, G., Bisdas, S., Brown, J., Cabezas, M., Cao, S., Cardoso, J. M., Carver, E. N., Casamitjana, A., Castillo, L. S., Catà, M., Cattin, P., Cerigues, A., Chagas, V. S., Chandra, S., Chang, Y.-J., Chang, S., Chang, K., Chazalon, J., Chen, S., Chen, W., Chen, J. W., Chen, Z., Cheng, K., Choudhury, A. R., Chylla, R., Clérigues, A., Colleman, S., Colmeiro, R. G. R., Combalia, M., Costa, A., Cui, X., Dai, Z., Dai, L., Daza, L. A., Deutsch, E., Ding, C., Dong, C., Dong, S., Dudzik, W. et al. (2019) · 2019
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., & Vercauteren, T. (2019) · 2019
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Multi-view semi-supervised 3d whole brain segmentation with a self-ensemble network
Zhao, Y.-X., Zhang, Y.-M., Song, M., & Liu, C.-L. (2019) · 2019
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Adaen-net: An ensemble of adaptive 2d–3d fully convolutional networks for medical image segmentation
Calisto, M. B., & Lai-Yuen, S. K. (2020) · 2020
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Brain tumor segmentation using an ensemble of 3d u-nets and overall survival prediction using radiomic features
Feng, X., Tustison, N. J., Patel, S. H., & Meyer, C. H. (2020) · 2020
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Brats toolkit: translating brats brain tumor segmentation algorithms into clinical and scientific practice
Kofler, F., Berger, C., Waldmannstetter, D., Lipkova, J., Ezhov, I., Tetteh, G., Kirschke, J., Zimmer, C., Wiestler, B., & Menze, B. H. (2020) · 2020
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Cited alongside, same era.
Phiseg: Capturing uncertainty in medical image segmentation
Baumgartner, C. F., Tezcan, K. C., Chaitanya, K., Hötker, A. M., Muehlematter, U. J., Schawkat, K., Becker, A. S., Donati, O., & Konukoglu, E. (2019) · 2019
Cited alongside, same era.
Inter-observer variability of manual contour delineation of structures in ct
Joskowicz, L., Cohen, D., Caplan, N., & Sosna, J. (2019) · 2019
Cited alongside, same era.
Triplanar ensemble of 3d-to-2d cnns with label-uncertainty for brain tumor segmentation
McKinley, R., Rebsamen, M., Meier, R., & Wiest, R. (2020) · 2019
Cited alongside, same era.
Bayesian quicknat: Model uncertainty in deep whole-brain segmentation for structure-wise quality control
Roy, A. G., Conjeti, S., Navab, N., Wachinger, C., Initiative, A. D. N. et al. (2019) · 2019
Cited alongside, same era.
Robust, primitive, and unsupervised quality estimation for segmentation ensembles
Kofler, F., Ezhov, I., Fidon, L., Pirkl, C. M., Paetzold, J. C., Burian, E., Pati, S., El Husseini, M., Navarro, F., Shit, S. et al. (2021a)
Cited in the paper.
Kofler, F., Ezhov, I., Isensee, F., Balsiger, F., Berger, C., Koerner, M., Paetzold, J., Li, H., Shit, S., McKinley, R. et al. (2021b)
Cited in the paper.
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Uncertainty quantification using bayesian neural networks in classification: Application to biomedical image segmentation
Kwon, Y., Won, J.-H., Kim, B. J., & Paik, M. C. (2020) · 2020
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Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty
Monteiro, M., Le Folgoc, L., Coelho de Castro, D., Pawlowski, N., Marques, B., Kamnitsas, K., van der Wilk, M., & Glocker, B. (2020) · 2020
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Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation
Nair, T., Precup, D., Arnold, D. L., & Arbel, T. (2020) · 2020
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Approaching peak ground truth
Kofler, F., Wahle, J., Ezhov, I., Wagner, S. J., Al-Maskari, R., Gryska, E., Todorov, M., Bukas, C., Meissen, F., Peng, T. et al. (2023) · 2023
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