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:精密缺血性分解在改善缺血性中风这一死亡率最高流行疾病的诊断和治疗规划方面起着重要作用。虽然最近提出了许多深神经网络方法来解决这一问题,但这些方法在培训过程中需要大量的附加说明的区域,在附加说明的数据稀少的医疗领域可能不切实际。作为一种补救措施,我们只使用一个附加说明的样本,对缺血性分解提出一种典型的微小分解方法。拟议方法利用一种针对缺血性中风分解任务的新型自我监督培训机制,利用通过测算成形成形透析产生的色标参数图。我们介绍了我们拟议培训机制的好处,这导致几发形图的性能得到显著改善。如果只有一个附加说明的病人,则在分解缺血性损伤方面平均达到0.58迪斯分位数。</s>