针对心脏MRI分割边界模糊问题，提出新型轻量级解码器提升精度。
CardioSAM: Topology-Aware Decoder Design for High-Precision Cardiac MRI Segmentation
- 融合SAM特征与心脏拓扑先验的专用解码器
- 在ACDC数据集上达到93.39%的Dice系数
- 适合临床医生用于高精度心脏结构分割
心血管磁共振（CMR）图像中心脏结构的精确分割对疾病诊断和治疗至关重要。然而，人工分割耗时且存在显著观察者间差异。尽管深度学习尤其是基础模型如分割一切模型（SAM）具备良好泛化能力，但临床应用所需的边界精度仍不足。为此，我们提出CardioSAM，一种混合架构：采用冻结的SAM编码器提取通用特征，搭配一个轻量级可训练的心脏专用解码器。该解码器引入两个关键创新：心脏特定注意力模块，融入解剖拓扑先验；边界精修模块，优化组织界面划分。在ACDC基准测试中，CardioSAM实现Dice系数93.39%、IoU 87.61%、像素准确率99.20%、HD95为4.2 mm。相比nnU-Net提升+3.89% Dice，超过已报道的专家间一致性水平（91.2%），展现出可靠且适用于临床的心脏分割潜力。
原文摘要 · Abstract (English)
Accurate segmentation of cardiac structures in cardiovascular magnetic resonance (CMR) images is essential for reliable diagnosis and treatment of cardiovascular diseases. However, manual segmentation remains time-consuming and suffers from significant inter-observer variability. Recent advances in deep learning, particularly foundation models such as the Segment Anything Model (SAM), demonstrate strong generalization but often lack the boundary precision required for clinical applications. To address this limitation, we propose CardioSAM, a hybrid architecture that combines the generalized feature extraction capability of a frozen SAM encoder with a lightweight, trainable cardiac-specific decoder. The proposed decoder introduces two key innovations: a Cardiac-Specific Attention module that incorporates anatomical topological priors, and a Boundary Refinement Module designed to improve tissue interface delineation. Experimental evaluation on the ACDC benchmark demonstrates that CardioSAM achieves a Dice coefficient of 93.39%, IoU of 87.61%, pixel accuracy of 99.20%, and HD95 of 4.2 mm. The proposed method surpasses strong baselines such as nnU-Net by +3.89% Dice and exceeds reported inter-expert agreement levels (91.2%), indicating its potential for reliable and clinically applicable cardiac segmentation.
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