Cardiovascular disease is a major global health concern, contributing significantly to global mortality. Accurately segmenting cardiac medical imaging data is crucial for reducing fatality rates associated with these conditions. However, current state-of-the-art (SOTA) neural networks, including CNN-based and Transformer-based approaches, face challenges in capturing both inter-slice connections and intra-slice details, especially in datasets featuring intricate, long-range details along the z-axis like coronary arteries. Existing methods also struggle with differentiating non-cardiac components from the myocardium, resulting in segmentation inaccuracies and the "spraying" phenomenon. To address these issues, we introduce RotCAtt-TransUNet++, a novel architecture designed for robust segmentation of intricate cardiac structures. Our approach enhances global context modeling through multiscale feature aggregation and nested skip connections in the encoder. Transformer layers facilitate capturing intra-slice interactions, while a rotatory attention mechanism handles inter-slice connectivity. A channel-wise cross-attention gate integrates multiscale information and decoder features, effectively bridging semantic gaps. Experimental results across multiple datasets demonstrate superior performance over current methods, achieving near-perfect annotation of coronary arteries and myocardium. Ablation studies confirm that our rotatory attention mechanism significantly improves segmentation accuracy by transforming embedded vectorized patches in semantic dimensional space.

本研究针对心血管疾病相关医疗成像数据的精确分割问题，提出了RotCAtt-TransUNet++新架构，以克服现有方法在捕捉切片间连接和切片内细节方面的限制。通过多尺度特征聚合和旋转注意力机制，本方法提高了心脏结构的分割准确性，实验结果显示在冠状动脉和心肌的注释中达到了近乎完美的效果。

RotCAtt-TransUNet++：一种新型深度神经网络用于复杂的心脏分割