Search results

• 2023

  CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation

  Dorent, R., Kujawa, A., Ivory, M., Bakas, S., Rieke, N., Joutard, S., Glocker, B., Cardoso, J., Modat, M., Batmanghelich, K., Belkov, A., Calisto, M. B., Choi, J. W., Dawant, B. M., Dong, H., Escalera, S., Fan, Y., Hansen, L., Heinrich, M. P., Joshi, S., & 20 othersKashtanova, V., Kim, H. G., Kondo, S., Kruse, C. N., Lai-Yuen, S. K., Li, H., Liu, H., Ly, B., Oguz, I., Shin, H., Shirokikh, B., Su, Z., Wang, G., Wu, J., Xu, Y., Yao, K., Zhang, L., Ourselin, S., Shapey, J. & Vercauteren, T., 1 Jan 2023, In: Medical Image Analysis. 83, 102628.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Benchmark 100%
  • Segmentation 100%
  • Domain Adaptation 100%
  • Vestibular Schwannoma 100%
  • Cochlea 100%
  1 Citation (Scopus)

  2 Downloads (Pure)
• 2020

  An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI

  Ebner, M., Wang, G., Li, W., Aertsen, M., Patel, P. A., Aughwane, R., Melbourne, A., Doel, T., Dymarkowski, S., De Coppi, P., David, A. L., Deprest, J., Ourselin, S. & Vercauteren, T., 1 Feb 2020, In: NeuroImage. 206, 116324.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Segmentation 100%
  • Brain 100%
  • Magnetic Resonance Imaging 100%
  • Convolutional Neural Network 20%
  • Standards 10%
  81 Citations (Scopus)

  157 Downloads (Pure)

• CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

  Gu, R., Wang, G., Song, T., Huang, R., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T. & Zhang, S., 2 Nov 2020, (E-pub ahead of print) In: IEEE transactions on medical imaging.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Image Segmentation 100%
  • Convolutional Neural Network 100%
  • Channels 66%
  • Segmentation 50%
  • Networks 33%
  98 Citations (Scopus)
• 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., 21 Apr 2019, In: NEUROCOMPUTING. 338, p. 34-45 12 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Image Segmentation 100%
  • Segmentation 100%
  • Convolutional Neural Network 100%
  • Models 50%
  • Model Uncertainty 50%
  210 Citations (Scopus)

• An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI

  Shapey, J., Wang, G., Dorent, R., Dimitriadis, A., Li, W., Paddick, I., Kitchen, N., Bisdas, S., Saeed, S. R., Ourselin, S., Bradford, R. & Vercauteren, T., 6 Dec 2019, (E-pub ahead of print) In: Journal of Neurosurgery. p. 1-9 9 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Magnetic Resonance Imaging 100%
  • Intelligence 100%
  • Vestibular Schwannoma 100%
  • Volumetry 100%
  • Patient 37%
  28 Citations (Scopus)

  25 Downloads (Pure)

• Automatic Brain Tumor Segmentation Based on Cascaded Convolutional Neural Networks With Uncertainty Estimation

  Wang, G., Li, W., Ourselin, S. & Vercauteren, T., 13 Aug 2019, In: Frontiers in Computational Neuroscience. 13, 56.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Segmentation 100%
  • Tumor Segmentation 100%
  • Convolutional Neural Network 100%
  • Brain Tumor 100%
  • Accuracy 60%
  108 Citations (Scopus)

  172 Downloads (Pure)
• 2018

  An Automated Localization, Segmentation and Reconstruction Framework for Fetal Brain MRI

  Ebner, M., Wang, G., Li, W., Aertsen, M., Patel, P. A., Aughwane, R., Melbourne, A., Doel, T., David, A. L., Deprest, J., Ourselin, S. & Vercauteren, T., 2018, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Schnabel, J. A., Davatzikos, C., Alberola-López, C., Fichtinger, G. & Frangi, A. F. (eds.). Springer Verlag, Vol. 11070. p. 313-320 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11070 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  • Segmentation 100%
  • Brain 100%
  • Magnetic Resonance Imaging 100%
  • Convolutional Neural Network 28%
  • Standards 14%
  27 Citations (Scopus)

• Placenta Imaging Workshop 2018 report: Multiscale and multimodal approaches

  Slator, P., Aughwane, R., Cade, G., Taylor, D., David, A. L., Lewis, R., Jauniaux, E., Desjardins, A., Salomon, L. J., Millischer, A-E., Tsatsaris, V., Rutherford, M., Johnstone, E., Melbourne, A., Atkinson, D., Baranikumar, R., Bertholdt, C., Bonet-Carne, E., Brownbill, P., Bruchhage, M., & 42 othersCaulfield, R., Chernyavsky, I., Chew, A., David, A., De Vita, E., Doel, T., Erlich, A., Flouri, D., Guerreri, M., Hakim, M., Hansen, D., Haq, M., Haris, P., Hillman, S., Ho, A., Hutter, J., Jackson, L., Johnstone, E., Kipergil, E., Labianco, S., Malamateniou, C., Maneas, E., Monton, E., Morris, D., Nihouarn, J., Nye, G., O'Neill, H., Thunbo, M. Ø., Palombo, M., Peasley, R., Baruteau, K. P., Mayo, R. P., Port, S., Salomon, L., Shah, S., Soe, N., Soerensen, A., Sokolska, M., Svigilsky, C., Tropea, T., Wang, G. & Yassine, B., 31 Oct 2018, (E-pub ahead of print) In: Placenta. 79, p. 78-82

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Workshop 100%
  • Placenta 100%
  • Multimodal Approach 100%
  • Posters 75%
  • Learning 50%
  5 Citations (Scopus)

  100 Downloads (Pure)