Acute ischemic stroke (AIS) is a common brain disease worldwide, and diagnosing AIS requires effectively utilizing information from multiple Computed Tomography Perfusion (CTP) maps. As far as we know, most methods independently process each CTP map or fail to fully utilize medical prior information when integrating the information from CTP maps. Considering the characteristics of AIS lesions, we propose a method for efficient information fusion of CTP maps to achieve accurate segmentation results. We propose Window Multi-Head Cross-Attention Net (WMHCA-Net), which employs a multi-path U-shaped architecture for encoding and decoding. After encoding, multiple independent windowed cross-attentions are used to deeply integrate information from different maps. During the decoding phase, a Channel Cross-Attention (CCA) module is utilized to enhance information recovery during upsampling. We also added a segmentation optimization module to optimize low-resolution segmentation results, improving the overall performance. Finally, experimental results demonstrate that our proposed method exhibits strong balance and excels across multiple metrics. It can provide more accurate AIS lesion segmentation results to assist doctors in evaluating patient conditions. Our code are available at https://github.com/MTVLab/WMHCA-Net.

中文翻译：

---

基于深度特征融合的急性缺血性卒中病灶分割


急性缺血性卒中 （AIS） 是世界范围内常见的脑部疾病，诊断 AIS 需要有效利用来自多个计算机断层扫描灌注 （CTP） 图的信息。据我们所知，大多数方法独立处理每个 CTP 图，或者在整合 CTP 图中的信息时未能充分利用医学先验信息。考虑到 AIS 病灶的特点，我们提出了一种 CTP 图的高效信息融合方法，以获得准确的分割结果。我们提出了窗口多头交叉注意力网 （WMHCA-Net），它采用多路径 U 形架构进行编码和解码。编码后，使用多个独立的窗叉注意力，将不同导图的信息深度融合。在解码阶段，利用通道交叉注意力 （CCA） 模块来增强上采样期间的信息恢复。我们还添加了一个分割优化模块来优化低分辨率分割结果，从而提高整体性能。最后，实验结果表明，我们提出的方法在多个指标上表现出很强的平衡性和优异性。它可以提供更准确的 AIS 病灶分割结果，以帮助医生评估患者状况。我们的代码可在 https://github.com/MTVLab/WMHCA-Net 上找到。