Abstract：
Many clinical and research studies of the human brain require accurate structural MRI segmentation. While traditional atlas-based methods can be applied to volumes from any acquisition site, recent deep learning algorithms ensure high accuracy only when tested on data from the same sites exploited in training (i.e., internal data). Performance degradation experienced on external data (i.e., unseen volumes from unseen sites) is due to the inter-site variability in intensity distributions, and to unique artefacts caused by different MR scanner models and acquisition parameters. 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 for learning a robust anatomical prior helpful in 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 unprecedentedly 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 paves the way for large-scale applications across different healthcare institutions, patient populations, and imaging technology manufacturers. Code, model, and demo are available on the project website.
摘要：
人脑的许多临床和研究研究都需要精确的结构MRI分割。虽然传统的基于图谱的方法可以应用于任何采集站点的卷，最近的深度学习算法仅在对来自训练中利用的相同站点的数据进行测试时才能确保高准确性(即，内部数据)。外部数据的性能下降(即，来自看不见的站点的看不见的体积)是由于强度分布的站点间可变性，以及由不同的MR扫描仪模型和采集参数引起的独特伪影。为了减轻这种站点依赖性，通常被称为扫描仪效果，我们建议LOD-大脑，具有渐进细节水平(LOD)的3D卷积神经网络，能够从任何地点分割大脑数据。较粗的网络水平负责学习强大的解剖学先验，有助于识别大脑结构及其位置，而更精细的水平完善模型来处理特定部位的强度分布和解剖变化。我们通过在前所未有的丰富数据集上训练模型来确保跨站点的鲁棒性，该数据集从开放的存储库汇总数据：来自约160个采集站点的近27,000个T1w卷，在1.5-3T，从8岁到90岁的人口。广泛的测试表明，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着差异，和强大的挑战性的解剖变化。它的便携性为跨不同医疗机构的大规模应用铺平了道路。患者群体，和成像技术制造商。代码,模型，和演示可以在项目网站上找到。
