Search results

• 2024

  Deep learning for automatic segmentation of vestibular schwannoma: a retrospective study from multi-center routine MRI

  Kujawa, A., Dorent, R., Connor, S., Thomson, S., Ivory, M., Vahedi, A., Guilhem, E., Wijethilake, N., Bradford, R., Kitchen, N., Bisdas, S., Ourselin, S., Vercauteren, T. & Shapey, J., 9 May 2024, In: Frontiers in Computational Neuroscience. 18, 1365727.

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

  Open Access

  • Automatic Segmentation 100%
  • Magnetic Resonance Imaging 100%
  • Acoustic Neuroma 100%
  • Segmentation Model 42%
  • Segmentation 28%
  2 Citations (Scopus)

• LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification

  Dorent, R., Khajavi, R., Idris, T., Ziegler, E., Somarouthu, B., Jacene, H., LaCasce, A., Deissler, J., Ehrhardt, J., Engelson, S., Fischer, S. M., Gu, Y., Handels, H., Kasai, S., Kondo, S., Maier-Hein, K., Schnabel, J. A., Wang, G., Wang, L. & Wald, T. & 7 others, Yang, G.-Z., Zhang, H., Zhang, M., Pieper, S., Harris, G., Kikinis, R. & Kapur, T., 19 Aug 2024, In: Machine.Learning.for.Biomedical.Imaging..

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

  File
  4 Downloads (Pure)

• MedShapeNet - a large-scale dataset of 3D medical shapes for computer vision.

  Li, J., Zhou, Z., Yang, J., Pepe, A., Gsaxner, C., Luijten, G., Qu, C., Zhang, T., Chen, X., Li, W., Wodzinski, M., Friedrich, P., Xie, K., Jin, Y., Ambigapathy, N., Nasca, E., Solak, N., Melito, G. M., Vu, V. D. & Memon, A. R. & 137 others, Schlachta, C., De Ribaupierre, S., Patel, R., Eagleson, R., Chen, X., Mächler, H., Kirschke, J. S., de la Rosa, E., Christ, P. F., Li, H. B., Ellis, D. G., Aizenberg, M. R., Gatidis, S., Küstner, T., Shusharina, N., Heller, N., Andrearczyk, V., Depeursinge, A., Hatt, M., Sekuboyina, A., Löffler, M. T., Liebl, H., Dorent, R., Vercauteren, T., Shapey, J., Kujawa, A., Cornelissen, S., Langenhuizen, P., Ben-Hamadou, A., Rekik, A., Pujades, S., Boyer, E., Bolelli, F., Grana, C., Lumetti, L., Salehi, H., Ma, J., Zhang, Y., Gharleghi, R., Beier, S., Sowmya, A., Garza-Villarreal, E. A., Balducci, T., Angeles-Valdez, D., Souza, R., Rittner, L., Frayne, R., Ji, Y., Ferrari, V., Chatterjee, S., Dubost, F., Schreiber, S., Mattern, H., Speck, O., Haehn, D., John, C., Nürnberger, A., Pedrosa, J., Ferreira, C., Aresta, G., Cunha, A., Campilho, A., Suter, Y., Garcia, J., Lalande, A., Vandenbossche, V., Van Oevelen, A., Duquesne, K., Mekhzoum, H., Vandemeulebroucke, J., Audenaert, E., Krebs, C., van Leeuwen, T., Vereecke, E., Heidemeyer, H., Röhrig, R., Hölzle, F., Badeli, V., Krieger, K., Gunzer, M., Chen, J., van Meegdenburg, T., Dada, A., Balzer, M., Fragemann, J., Jonske, F., Rempe, M., Malorodov, S., Bahnsen, F. H., Seibold, C., Jaus, A., Marinov, Z., Jaeger, P. F., Stiefelhagen, R., Santos, A. S., Lindo, M., Ferreira, A., Alves, V., Kamp, M., Abourayya, A., Nensa, F., Hörst, F., Brehmer, A., Heine, L., Hanusrichter, Y., Weßling, M., Dudda, M., Podleska, L. E., Fink, M. A., Keyl, J., Tserpes, K., Kim, M.-S., Elhabian, S., Lamecker, H., Zukić, D., Paniagua, B., Wachinger, C., Urschler, M., Duong, L., Wasserthal, J., Hoyer, P. F., Basu, O., Maal, T., Witjes, M. J. H., Schiele, G., Chang, T.-C., Ahmadi, S.-A., Luo, P., Menze, B., Reyes, M., Deserno, T. M., Davatzikos, C., Puladi, B., Fua, P., Yuille, A. L., Kleesiek, J. & Egger, J., 30 Dec 2024, (E-pub ahead of print) In: Biomedizinische Technik. Biomedical engineering.

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

  • Computer Vision 100%
  • Three Dimensional Printing 100%
  • Augmented Reality 50%
  • Medical Imaging 50%
  • Use Case 50%
• 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 others, Kashtanova, 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

  • Domain Adaptation 100%
  • Segmentation 100%
  • Vestibular Schwannoma 100%
  • Cochlea 100%
  • Acoustic Neuroma 100%
  45 Citations (Scopus)

  2 Downloads (Pure)
• 2022

  Automated Koos classification of Vestibular Schwannoma

  Kujawa, A., Dorent, R., Connor, S., Oviedova, A., Okasha, M., Grishchuk, D., Ourselin, S., Paddick, I., Kitchen, N., Vercauteren, T. & Shapey, J., 2022, In: Frontiers in Radiology.

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

  Open Access
  10 Citations (Scopus)

• FastGeodis: Fast Generalised Geodesic Distance Transform

  Asad, M., Dorent, R. & Vercauteren, T., 26 Jul 2022, In: Journal of Open Source Software.

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

  File
  48 Downloads (Pure)
• 2021

  Artificial Intelligence Opportunities for Vestibular Schwannoma Management Using Image Segmentation and Clinical Decision Tools

  Shapey, J., Kujawa, A., Dorent, R., Saeed, S. R., Kitchen, N., Obholzer, R., Ourselin, S., Vercauteren, T. & Thomas, N. W. M., May 2021, In: World Neurosurgery. 149, p. 269-270 2 p.

  Research output: Contribution to journal › Article › peer-review
  10 Citations (Scopus)

• A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections

  Pérez-García, F., Dorent, R., Rizzi, M., Cardinale, F., Frazzini, V., Navarro, V., Essert, C., Ollivier, I., Vercauteren, T., Sparks, R., Duncan, J. S. & Ourselin, S., Oct 2021, In: International Journal of Computer Assisted Radiology and Surgery. 16, 10, p. 1653-1661 9 p.

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

  Open Access

  • Self-Supervised Learning 100%
  • Supervised Learning Strategy 100%
  • Segmentation 100%
  • Brain 100%
  • Magnetic Resonance Imaging 100%
  18 Citations (Scopus)

• Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets

  Dorent, R., Booth, T., Li, W., Sudre, C. H., Kafiabadi, S., Cardoso, M. J., Ourselin, S. & Vercauteren, T., Jan 2021, In: Medical Image Analysis. 67, 101862.

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

  Open Access

  • Segmentation 100%
  • Brain Lesion 100%
  • White Matter 50%
  • Building-Blocks 33%
  • Quantitative Assessment 33%
  24 Citations (Scopus)
• 2019

  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%
  • Vestibular Schwannoma 100%
  • Volumetry 100%
  • Automatic Segmentation 100%
  • Acoustic Neuroma 100%
  77 Citations (Scopus)

  73 Downloads (Pure)