Can Segmentation Models Be Trained with Fully Synthetically Generated Data?

Autor: Virginia Fernandez, Walter Hugo Lopez Pinaya, Pedro Borges, Petru-Daniel Tudosiu, Mark S. Graham, Tom Vercauteren, M. Jorge Cardoso

Publikováno v: Simulation and Synthesis in Medical Imaging ISBN: 9783031169793

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ebc6f60ea65ead6e4984e4c6bbca33a1
https://doi.org/10.1007/978-3-031-16980-9_8

ICAM-reg:Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

Autor: Cher Bass, Mariana da Silva, Carole Sudre, Logan Z. J. Williams, Helena S. Sousa, Petru-Daniel Tudosiu, Fidel Alfaro-Almagro, Sean P. Fitzgibbon, Matthew F. Glasser, Stephen M. Smith, Emma C Robinson

Publikováno v: Bass, C, da Silva, M, Sudre, C, Williams, L Z J, Sousa, H S, Tudosiu, P-D, Alfaro-Almagro, F, Fitzgibbon, S P, Glasser, M F, Smith, S M & Robinson, E C 2022, ' ICAM-reg : Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans ', IEEE Transactions on Medical Imaging, pp. 1 . https://doi.org/10.1109/TMI.2022.3221890
IEEE Transactions on Medical Imaging. Institute of Electrical and Electronics Engineers Inc.

An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c29bbe94b0b72dd6226690ef2b4351fb
https://research.vumc.nl/en/publications/155ea408-bf90-4fb3-aede-261ff4e17adf

Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain

Autor: Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Mark S. Graham, Pedro Borges, Virginia Fernandez, Dai Yang, Jeremy Appleyard, Guido Novati, Disha Mehra, Mike Vella, Parashkev Nachev, Sebastien Ourselin, Jorge Cardoso

Publikováno v: Simulation and Synthesis in Medical Imaging ISBN: 9783031169793

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a517e19035dad4461ae0db7835f6049f
https://doi.org/10.1007/978-3-031-16980-9_7

Unsupervised brain imaging 3D anomaly detection and segmentation with transformers

Autor: Walter H.L. Pinaya, Petru-Daniel Tudosiu, Robert Gray, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

Publikováno v: Pinaya, W H L, Tudosiu, P-D, Gray, R, Rees, G, Nachev, P, Ourselin, S & Cardoso, M J 2022, ' Unsupervised Brain Imaging 3D Anomaly Detection and Segmentation with Transformers ', Medical Image Analysis, vol. 79, 102475 . https://doi.org/10.1016/j.media.2022.102475

Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific set of pathological features. Amongst the hardest tasks in medical imaging, detecting s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3775e148e17f9ba6085de3b19058632
https://pubmed.ncbi.nlm.nih.gov/35598520

Hierarchical Brain Parcellation with Uncertainty

Autor: Parashkev Nachev, Petru-Daniel Tudosiu, Thomas Varsavsky, M. Jorge Cardoso, Sebastien Ourselin, Mark S. Graham, Carole H. Sudre

Publikováno v: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis ISBN: 9783030603649
UNSURE/GRAIL@MICCAI

Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods tend to ignore this structure and treat labels as if they are ‘flat’.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9e4253472a7555b0453d2551965bb80e
https://doi.org/10.1007/978-3-030-60365-6_3