Department of Medical Physics and Biomedical Engineering, UCL, UK; School of Biomedical Engineering and Imaging Sciences, KCL, UK.
Department of Medical Physics and Biomedical Engineering, UCL, UK; School of Biomedical Engineering and Imaging Sciences, KCL, UK.
Med Image Anal. 2024 Feb;92:103058. doi: 10.1016/j.media.2023.103058. Epub 2023 Dec 7.
Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and, therefore, any downstream analyses. Post-hoc multi-site correction methods exist but have strong assumptions that often do not hold in real-world scenarios. Algorithms should be designed in a way that can account for site-specific effects, such as those that arise from sequence parameter choices, and in instances where generalisation fails, should be able to identify such a failure by means of explicit uncertainty modelling. This body of work showcases such an algorithm that can become robust to the physics of acquisition in the context of segmentation tasks while simultaneously modelling uncertainty. We demonstrate that our method not only generalises to complete holdout datasets, preserving segmentation quality but does so while also accounting for site-specific sequence choices, which also allows it to perform as a harmonisation tool.
合并多站点数据可以增强和揭示趋势，但这是一项受到特定于站点的协变量影响的任务，这些协变量可能会使数据产生偏差，从而影响任何下游分析。事后多站点校正方法虽然存在，但存在很强的假设，这些假设在实际情况下往往不成立。算法的设计应该考虑到特定于站点的影响，例如那些由序列参数选择引起的影响，并且在泛化失败的情况下，应该能够通过显式不确定性建模来识别这种失败。这项工作展示了一种算法，该算法在分割任务中可以在同时建模不确定性的情况下，对采集的物理特性具有鲁棒性。我们证明，我们的方法不仅可以推广到完整的保留数据集，保持分割质量，而且还可以考虑特定于站点的序列选择，这也使其可以用作协调工具。
