Medical image segmentation is crucial for clinical diagnosis and treatment planning, especially when dealing with complex anatomical structures such as vessels. However, accurately segmenting vessels remains challenging due to their small size, intricate edge structures, and susceptibility to artifacts and imaging noise. In this work, we propose VesselSAM, an enhanced version of the Segment Anything Model (SAM), specifically tailored for aortic vessel segmentation. VesselSAM incorporates AtrousLoRA, a novel module integrating Atrous Attention and Low-Rank Adaptation (LoRA), to enhance segmentation performance. Atrous Attention enables the model to capture multi-scale contextual information, preserving both fine-grained local details and broader global context. Additionally, LoRA facilitates efficient fine-tuning of the frozen SAM image encoder, reducing the number of trainable parameters and thereby enhancing computational efficiency. We evaluate VesselSAM using two challenging datasets: the Aortic Vessel Tree (AVT) dataset and the Type-B Aortic Dissection (TBAD) dataset. VesselSAM achieves state-of-the-art performance, attaining DSC scores of 93.50\%, 93.25\%, 93.02\%, and 93.26\% across multi-center datasets. 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和治疗规划至关重要，尤其是在处理如血管等复杂解剖结构时。然而，由于血管尺寸小、边缘结构复杂且易受伪影和成像噪声影响，准确分割血管仍具挑战性。本文提出VesselSAM，一种针对主动脉血管分割专门优化的增强版Segment Anything Model（SAM）。VesselSAM引入了AtrousLoRA模块，该模块融合了空洞注意力机制与低秩自适应技术，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，同时保留细粒度的局部细节和更广泛的全局上下文。此外，低秩自适应技术实现了对冻结SAM图像编码器的高效微调，减少了可训练参数数量，从而显著提升了计算效率。我们在两个具有挑战性的数据集上评估VesselSAM：主动脉血管树数据集和B型主动脉夹层数据集。VesselSAM在多中心数据集上取得了93.50%、93.25%、93.02%和93.26%的DSC分数，实现了最先进的性能。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，提供了高精度的分割结果。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。