Deep learning-based semantic segmentation in neuroimaging currently requires high-resolution scans and extensive annotated datasets, posing significant barriers to clinical applicability. We present a novel synthetic framework for the task of lesion segmentation, extending the capabilities of the established SynthSeg approach to accommodate large heterogeneous pathologies with lesion-specific augmentation strategies. Our method trains deep learning models, demonstrated here with the UNet architecture, using label maps derived from healthy and stroke datasets, facilitating the segmentation of both healthy tissue and pathological lesions without sequence-specific training data. Evaluated against in-domain and out-of-domain (OOD) datasets, our framework demonstrates robust performance, rivaling current methods within the training domain and significantly outperforming them on OOD data. This contribution holds promise for advancing medical imaging analysis in clinical settings, especially for stroke pathology, by enabling reliable segmentation across varied imaging sequences with reduced dependency on large annotated corpora. Code and weights available at https://github.com/liamchalcroft/SynthStroke.

翻译：基于深度学习的神经影像语义分割目前需要高分辨率扫描和大量标注数据集，这给临床适用性带来了重大障碍。我们提出了一种用于病灶分割任务的新型合成框架，扩展了已有SynthSeg方法的能力，通过病灶特异性增强策略适应大型异质性病理。我们的方法利用来自健康与脑卒中数据集的标签图训练深度学习模型（本文以UNet架构为例），无需序列特异性训练数据即可实现健康组织与病理病灶的分割。在域内和域外数据集上的评估表明，我们的框架展现出鲁棒性能，在训练域内可与现有方法媲美，并在域外数据上显著优于现有方法。这一贡献有望推动临床环境中的医学影像分析发展，尤其针对脑卒中病理，通过减少对大型标注语料库的依赖，实现不同影像序列中的可靠分割。代码和权重见https://github.com/liamchalcroft/SynthStroke。