Medical image segmentation is crucial for clinical diagnosis and treatment planning, particularly for complex anatomical structures like vessels. In this work, we propose VesselSAM, a modified version of the Segmentation Anything Model (SAM), specifically designed for aortic vessel segmentation. VesselSAM incorporates AtrousLoRA, a novel module that combines Atrous Attention with Low-Rank Adaptation (LoRA), to improve segmentation performance. Atrous Attention enables the model to capture multi-scale contextual information, preserving both fine local details and broader global context. At the same time, LoRA facilitates efficient fine-tuning of the frozen SAM image encoder, reducing the number of trainable parameters and ensuring computational efficiency. We evaluate VesselSAM on two challenging datasets: the Aortic Vessel Tree (AVT) dataset and the Type-B Aortic Dissection (TBAD) dataset. VesselSAM achieves state-of-the-art performance with DSC scores of 93.50\%, 93.25\%, 93.02\%, and 93.26\% across multiple medical centers. Our results demonstrate that VesselSAM delivers high segmentation accuracy while significantly reducing computational overhead compared to existing large-scale models. This development paves the way for enhanced AI-based aortic vessel segmentation in clinical environments. The code and models will be released at https://github.com/Adnan-CAS/AtrousLora.

翻译：医学图像分割对于临床诊断与治疗规划至关重要，尤其针对血管等复杂解剖结构。本文提出VesselSAM，一种基于分割万物模型（SAM）改进的版本，专门用于主动脉血管分割。VesselSAM引入了AtrousLoRA模块，该创新模块将空洞注意力与低秩自适应（LoRA）相结合，以提升分割性能。空洞注意力使模型能够捕获多尺度上下文信息，同时保留精细的局部细节与更广泛的全局上下文。与此同时，LoRA实现了对冻结SAM图像编码器的高效微调，减少了可训练参数数量并确保计算效率。我们在两个具有挑战性的数据集上评估VesselSAM：主动脉血管树（AVT）数据集和B型主动脉夹层（TBAD）数据集。VesselSAM在多个医疗中心取得了93.50%、93.25%、93.02%和93.26%的DSC分数，达到了最先进的性能。实验结果表明，与现有大规模模型相比，VesselSAM在显著降低计算开销的同时实现了高精度分割。这一进展为临床环境中基于人工智能的主动脉血管分割技术发展开辟了新途径。代码与模型将在https://github.com/Adnan-CAS/AtrousLora发布。