Pulmonary vein anatomical structure typing plays a crucial role in the preoperative assessment and postoperative evaluation of lung tumor resection, atrial fibrillation radio frequency ablation, and other medical procedures. The accuracy of such typing relies heavily on the segmentation results of the left atrium and proximal pulmonary veins. However, due to the similarities in intensity between the left atrium, proximal pulmonary veins, and adjacent tissues in CT images, segmentation errors often occur, leading to subsequent inaccuracies in pulmonary vein classification. To address this issue, we propose an attention module called Dimensional Decomposition Attention (DDA), which combines Dimensional Decomposition Spatial Attention (DDSA) and Dimensional Decomposition Channel Attention (DDCA). DDA effectively leverages the spatial and channel information of 3D images to enhance the segmentation accuracy of the left atrium and proximal pulmonary veins. In DDSA, the input features are decomposed into three one‐dimensional directional features (height, width, and depth) and fused to generate weights that emphasize spatial shape features and focus on the region of interest. On the other hand, DDCA encodes the input features into dimensional channel features, fuses them with one‐dimensional directional features, and utilizes position encoding to reinforce the channel features and prioritize channels with relevant information. The performance of DDA was evaluated using a two‐stage experimental approach on datasets provided by The People's Hospital of Liaoning Province and the MM‐WHS CT dataset, yielding average Dice values of 93.93% and 90.80%, respectively, demonstrating the effectiveness of DDA.

中文翻译：

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基于维度分解注意力的左心房和近端肺静脉分割

肺静脉解剖结构分型对于肺肿瘤切除、房颤射频消融等医疗手术的术前评估和术后评估起着至关重要的作用。这种分型的准确性在很大程度上依赖于左心房和近端肺静脉的分割结果。然而，由于CT图像中左心房、近端肺静脉和邻近组织之间的强度相似，经常出现分割错误，导致随后的肺静脉分类不准确。为了解决这个问题，我们提出了一种称为维度分解注意力（DDA）的注意力模块，它结合了维度分解空间注意力（DDSA）和维度分解通道注意力（DDCA）。 DDA有效利用3D图像的空间和通道信息来提高左心房和近端肺静脉的分割精度。在DDSA中，输入特征被分解为三个一维方向特征（高度、宽度和深度）并融合以生成强调空间形状特征并关注感兴趣区域的权重。另一方面，DDCA将输入特征编码为维度通道特征，将其与一维方向特征融合，并利用位置编码来强化通道特征并利用相关信息对通道进行优先级排序。在辽宁省人民医院提供的数据集和MM-WHS CT数据集上采用两阶段实验方法评估DDA的性能，平均Dice值分别为93.93%和90.80%，证明了DDA的有效性。