对抗脑MRI分割中的扫描仪效应：一种基于多站点数据训练的渐进式细节层次网络
Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data
AI总结：
针对脑MRI分割中的扫描仪效应，提出LOD-Brain渐进式细节层次3D CNN，利用多站点近27,000个T1w数据训练，实现跨站点最先进性能且内外站点无显著差异。
摘要：
许多人类大脑的临床和科学研究需要准确的结构性MRI分割。虽然传统的基于图谱的方法可以应用于来自任何采集站点的体数据，但最近的深度学习算法只有在测试来自训练中使用的同一站点的数据（即内部数据）时才能确保非常高的准确性。在外部数据（即来自未见站点的未见体数据）上经历的性能下降是由于不同MR扫描仪型号、采集参数和独特伪影引起的站点间强度分布差异。为了减轻这种通常被称为扫描仪效应的站点依赖性，我们提出了LOD-Brain，一种具有渐进式细节层次（LOD）的3D卷积神经网络，能够分割来自任何站点的脑数据。较粗的网络层次负责学习稳健的解剖先验，有助于识别脑结构及其位置，而较细的层次则细化模型以处理特定站点的强度分布和解剖变异。我们通过在一个前所未有的丰富数据集上训练模型来确保跨站点的稳健性，该数据集聚合了来自开放存储库的数据：来自约160个采集站点的近27,000个T1w体数据，场强为1.5-3T，人群年龄跨度从8岁到90岁。大量测试表明，LOD-Brain产生了最先进的结果，内部和外部站点之间的性能没有显著差异，并且对具有挑战性的解剖变异具有稳健性。其可移植性为在不同医疗机构、患者群体和影像技术制造商之间的大规模应用开辟了道路。代码、模型和演示可在项目网站上获取。
英文摘要：
Many clinical and research studies of the human brain require an accurate structural MRI segmentation. While traditional atlas-based methods can be applied to volumes from any acquisition site, recent deep learning algorithms ensure very high accuracy only when tested on data from the same sites exploited in training (i.e., internal data). The performance degradation experienced on external data (i.e., unseen volumes from unseen sites) is due to the inter-site variabilities in intensity distributions induced by different MR scanner models, acquisition parameters, and unique artefacts. To mitigate this site-dependency, often referred to as the scanner effect, we propose LOD-Brain, a 3D convolutional neural network with progressive levels-of-detail (LOD) able to segment brain data from any site. Coarser network levels are responsible to learn a robust anatomical prior useful for identifying brain structures and their locations, while finer levels refine the model to handle site-specific intensity distributions and anatomical variations. We ensure robustness across sites by training the model on an unprecedented rich dataset aggregating data from open repositories: almost 27,000 T1w volumes from around 160 acquisition sites, at 1.5 - 3T, from a population spanning from 8 to 90 years old. Extensive tests demonstrate that LOD-Brain produces state-of-the-art results, with no significant difference in performance between internal and external sites, and robust to challenging anatomical variations. Its portability opens the way for large scale application across different healthcare institutions, patient populations, and imaging technology manufacturers. Code, model, and demo are available at the project website.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
