ROOD-MRI

ROOD-MRI是由多伦多大学医学物理系开发的一个用于评估深度神经网络对MRI中分布外数据、损坏和伪影鲁棒性的基准平台。该数据集包含257个样本，主要用于海马体、脑室和白质高信号的分割任务。数据集通过模拟MRI中的分布偏移和损坏来生成，旨在解决现有模型在不同扫描仪和采集协议间性能下降的问题。ROOD-MRI提供了一个灵活的平台，用于生成基准数据集，实施新的基准度量，并使用新模型和任务进行示例。该数据集的应用领域包括神经退行性疾病和认知衰退的研究，以及在临床和研究环境中对各种神经疾病的诊断、预后和治疗规划。

ROOD-MRI is a benchmark platform developed by the Medical Physics Department of the University of Toronto for evaluating the robustness of deep neural networks against out-of-distribution data, corruptions and artifacts in MRI scans. This dataset consists of 257 samples, and is primarily designed for segmentation tasks of the hippocampus, ventricles and white matter hyperintensities. The dataset is generated by simulating distribution shifts and corruptions in MRI, aiming to address the performance degradation issue of existing models across different scanners and acquisition protocols. ROOD-MRI provides a flexible platform for generating benchmark datasets, implementing novel benchmark metrics, and conducting demonstrations with new models and tasks. The application scenarios of this dataset cover research on neurodegenerative diseases and cognitive decline, as well as the diagnosis, prognosis and treatment planning of various neurological diseases in clinical and research settings.