论文标题
强大的机器学习细分，用于对异质临床大脑MRI数据集进行大规模分析
Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
论文作者
论文摘要
每年都会在医院中获得数百万个大脑MRI扫描，这比任何研究数据集的规模都大得多。因此，分析此类扫描的能力可以改变神经成像研究。然而，由于没有自动化算法足以应对临床采集的高度可变性（MR对比，决议，方向，伪像，受试者人群），因此它们的潜力仍未开发。在这里，我们提出了Synthseg+，这是一个AI分割套件，该套件首次可以对异质临床数据集进行强有力的分析。除了全脑分割外，合成+还执行皮质细胞，颅内体积估计和自动检测故障分割（主要是由质量非常低的扫描引起的）。我们在七个实验中证明了合成++，包括对14,000张扫描的老化研究，在该研究中，它准确地复制了在质量更高的数据上观察到的萎缩模式。 Synthseg+公开发布，作为一种现成的工具，可以解锁定量形态计量学的潜力。
Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyse such scans could transform neuroimaging research. Yet, their potential remains untapped, since no automated algorithm is robust enough to cope with the high variability in clinical acquisitions (MR contrasts, resolutions, orientations, artefacts, subject populations). Here we present SynthSeg+, an AI segmentation suite that enables, for the first time, robust analysis of heterogeneous clinical datasets. In addition to whole-brain segmentation, SynthSeg+ also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations (mainly caused by scans of very low quality). We demonstrate SynthSeg+ in seven experiments, including an ageing study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality. SynthSeg+ is publicly released as a ready-to-use tool to unlock the potential of quantitative morphometry.
