White matter hyperintensities (WMH) and ischaemic stroke lesions (ISL) are imaging features associated with cerebral small vessel disease (SVD) that are visible on brain magnetic resonance imaging (MRI) scans. The development and validation of deep learning models to segment and differentiate these features is difficult because they visually confound each other in the fluid-attenuated inversion recovery (FLAIR) sequence and often appear in the same subject. We investigated six strategies for training a combined WMH and ISL segmentation model using partially labelled data. We combined privately held fully and partially labelled datasets with publicly available partially labelled datasets to yield a total of 2052 MRI volumes, with 1341 and 1152 containing ground truth annotations for WMH and ISL respectively. We found that several methods were able to effectively leverage the partially labelled data to improve model performance, with the use of pseudolabels yielding the best result.

翻译：白质高信号（WMH）与缺血性卒中病灶（ISL）是脑小血管病（SVD）相关的影像学特征，在脑磁共振成像（MRI）扫描中可见。由于这些特征在液体衰减反转恢复（FLAIR）序列中视觉上相互混淆，且常出现在同一受试者中，开发并验证能够分割与区分这些特征的深度学习模型颇具挑战。本研究探讨了六种利用部分标注数据训练WMH与ISL联合分割模型的策略。通过整合私有全标注与部分标注数据集以及公开可用的部分标注数据集，共获得2052个MRI容积数据，其中分别有1341个和1152个包含WMH与ISL的真实标注。研究发现，多种方法能有效利用部分标注数据提升模型性能，其中使用伪标签的方法取得了最佳结果。