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Research portal

Mr Cian Scannell

  1. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

    Campello, V. M., Gkontra, P., Izquierdo, C., Martín-Isla, C., Sojoudi, A., Full, P. M., Maier-Hein, K., Zhang, Y., He, Z., Ma, J., Parreño, M., Albiol, A., Kong, F., Shadden, S. C., Acero, J. C., Sundaresan, V., Saber, M., Elattar, M., Li, H., Menze, B. & 27 others, Khader, F., Haarburger, C., Scannell, C. M., Veta, M., Carscadden, A., Punithakumar, K., Liu, X., Tsaftaris, S. A., Huang, X., Yang, X., Li, L., Zhuang, X., Viladés, D., Descalzo, M. L., Guala, A., La Mura, L., Friedrich, M. G., Garg, R., Lebel, J., Henriques, F., Karakas, M., Çavuş, E., Petersen, S. E., Escalera, S., Seguí, S., Palomares, J. F. R. & Lekadir, K., 17 Jun 2021, In: IEEE Transactions on Medical Imaging. p. 1-1 1 p.

    Research output: Contribution to journalArticlepeer-review

  2. Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation

    Scannell, C. M., Chiribiri, A. & Veta, M., 2021, Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. Puyol Anton, E., Pop, M., Sermesant, M., Campello, V., Lalande, A., Lekadir, K., Suinesiaputra, A., Camara, O. & Young, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 228-237 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12592 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

  3. Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging

    Scannell, C., Matias Correia, T., Villa, A., Schneider, T., Lee, J., Breeuwer, M., Chiribiri, A. & Henningsson, M., 29 Jul 2020, In: Scientific Reports. 10, 12684 .

    Research output: Contribution to journalArticlepeer-review

  4. Deep‐Learning‐Based Preprocessing for Quantitative Myocardial Perfusion MRI

    Scannell, C. M., Veta, M., Villa, A. D. M., Sammut, E. C., Lee, J., Breeuwer, M. & Chiribiri, A., 1 Jun 2020, In: Journal of Magnetic Resonance Imaging. 51, 6, p. 1689-1696 8 p.

    Research output: Contribution to journalArticlepeer-review

  5. Hierarchical Bayesian myocardial perfusion quantification

    Scannell, C. M., Chiribiri, A., Villa, A. D. M., Breeuwer, M. & Lee, J., 1 Feb 2020, In: Medical Image Analysis. 60, 101611.

    Research output: Contribution to journalArticlepeer-review

  6. Robust non-rigid motion compensation of free-breathing myocardial perfusion MRI data

    Scannell, C. M., Villa, A., Lee, C. J., Breeuwer, M. & Chiribiri, A., 1 Aug 2019, In: IEEE Transactions on Medical Imaging. 38, 8, p. 1812-1820 9 p., 8632981.

    Research output: Contribution to journalArticlepeer-review

  7. Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI

    Scannell, C. M., van den Bosch, P., Chiribiri, A., Lee, C. J., Breeuwer, M. & Veta, M., 29 Jul 2019, Medical Imaging with Deep Learning: MIDL 2019.

    Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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