We propose a novel and flexible attention based U-Net architecture referred to as "Voxels-Intersecting Along Orthogonal Levels Attention U-Net" (viola-Unet), for intracranial hemorrhage (ICH) segmentation task in the INSTANCE 2022 Data Challenge on non-contrast computed tomography (CT). The performance of ICH segmentation was improved by efficiently incorporating fused spatially orthogonal and cross-channel features via our proposed Viola attention plugged into the U-Net decoding branches. The viola-Unet outperformed the strong baseline nnU-Net models during both 5-fold cross validation and online validation. Our solution was the winner of the challenge validation phase in terms of all four performance metrics (i.e., DSC, HD, NSD, and RVD). The code base, pretrained weights, and docker image of the viola-Unet AI tool are publicly available at \url{https://github.com/samleoqh/Viola-Unet}.

翻译：我们提出了一种新颖且灵活的基于注意力机制的U-Net架构，称为“Voxels-Intersecting Along Orthogonal Levels Attention U-Net”（viola-Unet），用于INSTANCE 2022数据挑战赛中非增强计算机断层扫描（CT）的颅内出血（ICH）分割任务。通过在U-Net解码分支中嵌入我们提出的Viola注意力机制，有效融合了空间正交和跨通道特征，从而提升了ICH分割性能。在5折交叉验证和在线验证中，viola-Unet均优于强大的基线nnU-Net模型。我们的解决方案在验证阶段的全部四项性能指标（即DSC、HD、NSD和RVD）上均夺得冠军。viola-Unet AI工具的代码库、预训练权重及Docker镜像已在 \url{https://github.com/samleoqh/Viola-Unet} 公开提供。