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：精确的缺血性病变分割在改善缺血性脑卒中（一种具有最高死亡率的常见疾病）的诊断和治疗规划中起着关键作用。尽管近年来已有大量深度神经网络方法被提出来解决这一问题，但这些方法在训练过程中需要大量标注区域，而在医学领域中标注数据稀缺的情况下，这并不实用。作为解决方案，我们提出了一种原型小样本分割方法，用于缺血性病变分割，只需在训练中使用一个标注样本。该方法利用了一种新颖的自监督训练机制，该机制通过利用从计算机断层扫描灌注扫描生成的彩色参数图，专门针对缺血性脑卒中病变分割任务进行定制。我们展示了所提出训练机制的优点，在小样本设置下实现了性能的显著提升。给定单个标注患者，缺血性病变分割的平均Dice得分为0.58。