Brain stroke has become a significant burden on global health and thus we
need remedies and prevention strategies to overcome this challenge. For this,
the immediate identification of stroke and risk stratification is the primary
task for clinicians. To aid expert clinicians, automated segmentation models
are crucial. In this work, we consider the publicly available dataset ATLAS
$v2.0$ to benchmark various end-to-end supervised U-Net style models.
Specifically, we have benchmarked models on both 2D and 3D brain images and
evaluated them using standard metrics. We have achieved the highest Dice score
of 0.583 on the 2D transformer-based model and 0.504 on the 3D residual U-Net
respectively. We have conducted the Wilcoxon test for 3D models to correlate
the relationship between predicted and actual stroke volume. For
reproducibility, the code and model weights are made publicly available:
this https URL

脑卒中已经成为全球健康负担，因此我们需要治疗方法和预防策略来克服这一挑战。在此工作中，我们考虑了公开可用的数据集 ATLAS v2.0，以评估各种端到端有监督的 U-Net 风格模型。我们在 2D 变换器模型上达到了最高的 Dice 得分 0.583，在 3D 残差 U-Net 模型上达到了 0.504。我们对 3D 模型进行了 Wilcoxon 测试，以确定预测和实际中风体积之间的关系。为了可复现性，代码和模型权重已公开提供。