Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation. We propose a novel strategy that performs segmentation using specialist networks that focus on a single anatomy (left ventricle, right ventricle, or myocardium). Given an input long-axis cardiac MR image, our method performs a ternary segmentation in the first stage to identify these anatomical regions, followed by cropping the original image to focus subsequent processing on the anatomical regions. The specialist networks are coupled through an attention mechanism that performs cross-attention to interlink features from different anatomies, serving as a soft relative shape prior. Central to our approach is an additive attention block (E-2A block), which is used throughout our architecture thanks to its efficiency.

翻译：心血管疾病的自动化诊断常依赖于心脏图像分割这一关键任务。我们提出了一种新策略，通过专注于单一解剖结构（左心室、右心室或心肌）的专业网络进行分割。对于输入的长轴心脏磁共振图像，该方法首先通过三元分割识别这些解剖区域，随后对原始图像进行裁剪，将后续处理聚焦于解剖区域。专业网络通过注意力机制耦合，该机制采用交叉注意力实现不同解剖结构特征的互联，起到软性相对形状先验的作用。本方法的核心是加性注意力模块（E-2A模块），由于其高效性，该模块被广泛应用于我们的架构中。