The cornerstone of stroke care is expedient management that varies depending on the time since stroke onset. Consequently, clinical decision making is centered on accurate knowledge of timing and often requires a radiologist to interpret Computed Tomography (CT) of the brain to confirm the occurrence and age of an event. These tasks are particularly challenging due to the subtle expression of acute ischemic lesions and the dynamic nature of their appearance. Automation efforts have not yet applied deep learning to estimate lesion age and treated these two tasks independently, so, have overlooked their inherent complementary relationship. To leverage this, we propose a novel end-to-end multi-task transformer-based network optimized for concurrent segmentation and age estimation of cerebral ischemic lesions. By utilizing gated positional self-attention and CT-specific data augmentation, the proposed method can capture long-range spatial dependencies while maintaining its ability to be trained from scratch under low-data regimes commonly found in medical imaging. Furthermore, to better combine multiple predictions, we incorporate uncertainty by utilizing quantile loss to facilitate estimating a probability density function of lesion age. The effectiveness of our model is then extensively evaluated on a clinical dataset consisting of 776 CT images from two medical centers. Experimental results demonstrate that our method obtains promising performance, with an area under the curve (AUC) of 0.933 for classifying lesion ages <=4.5 hours compared to 0.858 using a conventional approach, and outperforms task-specific state-of-the-art algorithms.

翻译：卒中治疗的核心在于根据发病时间进行快速管理。因此，临床决策制定依赖于对发病时间的准确认知，通常需要放射科医师解读脑部计算机断层扫描（CT）以确认事件的发生及其病程。由于急性缺血性病灶的影像学表现细微且其外观具有动态演变特征，这两项任务尤为艰巨。现有自动化方法尚未将深度学习应用于病灶年龄估计，且将这两项任务独立处理，从而忽视了其内在的互补关系。为利用这一特性，我们提出了一种新颖的端到端多任务Transformer网络，该网络针对脑缺血性病灶的同步分割与年龄估计进行了优化。通过采用门控位置自注意力机制和CT特异性数据增强技术，所提方法能够捕捉长距离空间依赖关系，同时保持其在医学影像常见的小样本场景下从零开始训练的能力。此外，为更好地融合多任务预测结果，我们利用分位数损失引入不确定性，以促进病灶年龄概率密度函数的估计。最后，我们在包含两个医学中心776张CT图像的临床数据集上全面验证了模型的有效性。实验结果表明，我们的方法取得了优异性能：在分类病灶年龄≤4.5小时的任务中，曲线下面积（AUC）达到0.933，而传统方法仅为0.858，并且超越了任务特定的最新算法。