Background and objective. Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment.

Methods. We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. We pre-process the data to facilitate learning features based on the symmetry of brain hemispheres. The issue of class imbalance is tackled using small patches with a balanced training patch sampling strategy and a dynamically weighted loss function. Moreover, a combination of whole patch predictions, using a U-Net based CNN architecture, and high degree of overlapping patches reduces the need for additional post-processing.

Results. The proposed method is evaluated using two public datasets from the 2015 Ischemic Stroke Lesion Segmentation challenge (ISLES 2015). These involve the tasks of sub-acute stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES) from multiple diffusion, perfusion and anatomical MRI modalities. The performance is compared against state-of-the-art methods with a blind online testing set evaluation on each of the challenges. At the time of submitting this manuscript, our approach is the first method in the online rankings for the SISS (DSC=0.59 ± 0.31) and SPES sub-tasks (DSC=0.84 ± 0.10). When compared with the rest of submitted strategies, we achieve top rank performance with a lower Hausdorff distance.

Conclusions. Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of class imbalance. The same training procedure is used for both tasks, showing the proposed methodology can generalize well enough to deal with different unrelated tasks and imaging modalities without hyper-parameter tuning. In order to promote the reproducibility of our results, a public version of the proposed method has been released to the scientific community.

背景和目标。急性中风病变分割任务具有很大的临床意义，因为它们可以帮助医生做出更明智的，对时间要求严格的治疗决策。磁共振成像（MRI）需要时间，但可以提供被认为是诊断金标准的图像。自动化的中风病灶分割可以提供病灶组织的位置和体积的估计值，这可以帮助临床实践更好地评估和评估每种治疗的风险。

方法。我们提出了一种深度学习方法，用于使用多模式MR成像进行急性和亚急性中风病灶分割。我们对数据进行预处理，以促进基于脑半球对称性的学习功能。使用具有均衡训练补丁采样策略和动态加权损失函数的小补丁来解决班级不平衡的问题。此外，使用基于U-Net的CNN架构将整个补丁预测与高度重叠的补丁相结合，可以减少对其他后期处理的需求。

结果。使用来自2015年缺血性卒中病变分割挑战（ISLES 2015）的两个公共数据集对提出的方法进行了评估。这些涉及亚急性中风病变分割（SISS）和急性中风半影估计（SPES）的任务，这些方法来自多种扩散，灌注和解剖学MRI模式。通过对每个挑战的盲目在线测试集评估，将性能与最新技术进行了比较。在提交此手稿时，我们的方法是SISS（DSC = 0.59±0.31）和SPES子任务（DSC = 0.84±0.10）在线排名中的第一种方法。与其他提交的策略相比，我们在较短的Hausdorff距离下获得了最高的性能。

结论。通过利用急性中风病灶的解剖学和病理生理学，并结合使用一种方法来最大程度地减少类别不平衡的影响，可以获得更好的分割结果。两种任务都使用相同的训练程序，这表明所提出的方法可以很好地泛化到足以处理不同的无关任务和成像模式而无需进行超参数调整。为了提高结果的可重复性，该方法的公开版本已发布给科学界。