用改进版SAM精准分割主动脉血管，提升精度还省算力。
VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with AtrousLoRA
- 引入AtrousLoRA模块，融合空洞注意力与低秩微调，增强多尺度特征捕捉。
- 在多个数据集上达到93.0%以上的分割骰子系数，优于现有方法。
- 适合临床医学影像分析，尤其对算力有限的场景友好。
医学图像分割对临床诊断和治疗规划至关重要，尤其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，准确分割仍具挑战。本文提出VesselSAM，一种专为升主动脉血管分割优化的Segment Anything Model（SAM）增强版本。VesselSAM引入AtrousLoRA模块，结合空洞注意力（Atrous Attention）与低秩适应（LoRA），以增强多尺度上下文信息捕获能力，同时保持局部细节与全局上下文。此外，LoRA实现冻结的SAM图像编码器高效微调，显著减少可训练参数，提升计算效率。我们在两个具有挑战性的数据集——升主动脉血管树（AVT）与Ⅱ型主动脉夹层（TBAD）上评估模型性能。VesselSAM在多中心数据集上取得93.50%、93.25%、93.02%和93.26%的骰子系数（DSC），达到当前最优水平。结果表明，VesselSAM在保证高分割精度的同时，大幅降低计算开销，为临床环境中基于AI的主动脉血管分割提供新路径。代码与模型将开源于https://github.com/Adnan-CAS/AtrousLora。
原文摘要 · Abstract (English)
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.
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