Precise ischemic lesion segmentation plays an essential role in improving diagnosis and treatment planning for ischemic stroke, one of the prevalent diseases with the highest mortality rate. While numerous deep neural network approaches have recently been proposed to tackle this problem, these methods require large amounts of annotated regions during training, which can be impractical in the medical domain where annotated data is scarce. As a remedy, we present a prototypical few-shot segmentation approach for ischemic lesion segmentation using only one annotated sample during training. The proposed approach leverages a novel self-supervised training mechanism that is tailored to the task of ischemic stroke lesion segmentation by exploiting color-coded parametric maps generated from Computed Tomography Perfusion scans. We illustrate the benefits of our proposed training mechanism, leading to considerable improvements in performance in the few-shot setting. Given a single annotated patient, an average Dice score of 0.58 is achieved for the segmentation of ischemic lesions.

翻译：精准的缺血性病变分割在改善缺血性脑卒中（一种致死率最高的常见疾病）的诊断与治疗计划中起着关键作用。尽管近来提出了众多深度神经网络方法来解决这一问题，但这些方法在训练过程中需要大量标注区域，而在标注数据稀缺的医学领域，这往往不切实际。为此，我们提出一种基于原型网络的少样本分割方法，仅需在训练中使用一个标注样本即可进行缺血性病变分割。所提方法利用一种新颖的自监督训练机制，该机制通过利用计算机断层灌注扫描生成的彩色编码参数图，专门针对缺血性脑卒中病变分割任务进行定制。我们展示了所提训练机制的优势，在少样本设定下带来了显著的性能提升。在仅提供一个标注患者样本的情况下，缺血性病变分割的平均Dice分数达到0.58。