标题： 通过短长时记忆SAM加速体积医学图像标注 显示英文标题

标题： Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2

摘要： 对磁共振成像（MRI）和计算机断层扫描（CT）等体积医学图像的手动标注是一项劳动密集且耗时的过程。视频对象分割基础模型的最新进展，例如Segment Anything Model 2（SAM 2），提供了一个潜在的机会，通过手动标注一个或几个切片，然后在整个体积内传播目标掩码，从而显著加快标注过程。然而，在这种情况下，SAM 2的表现存在差异。我们的实验表明，依赖单一记忆库和注意力模块容易导致错误传播，尤其是在目标在前一个切片中存在但在当前切片中不存在的边界区域。为了解决这个问题，我们提出了Short-Long Memory SAM 2（SLM-SAM 2），这是一种新颖的架构，集成了不同的短期和长期记忆库以及独立的注意力模块以提高分割准确性。我们在涵盖MRI和CT模态下器官、骨骼和肌肉的三个公开数据集上评估了SLM-SAM 2。我们展示了所提出的方法明显优于默认的SAM 2，在初始适应时分别有5个体积和1个体积可用的情况下，平均Dice相似系数分别提高了0.14和0.11。SLM-SAM 2还表现出更强的抗过传播能力，朝着更准确的医学图像自动标注迈出了重要一步，用于分割模型开发。 显示英文摘要

摘要： Manual annotation of volumetric medical images, such as magnetic resonance imaging (MRI) and computed tomography (CT), is a labor-intensive and time-consuming process. Recent advancements in foundation models for video object segmentation, such as Segment Anything Model 2 (SAM 2), offer a potential opportunity to significantly speed up the annotation process by manually annotating one or a few slices and then propagating target masks across the entire volume. However, the performance of SAM 2 in this context varies. Our experiments show that relying on a single memory bank and attention module is prone to error propagation, particularly at boundary regions where the target is present in the previous slice but absent in the current one. To address this problem, we propose Short-Long Memory SAM 2 (SLM-SAM 2), a novel architecture that integrates distinct short-term and long-term memory banks with separate attention modules to improve segmentation accuracy. We evaluate SLM-SAM 2 on three public datasets covering organs, bones, and muscles across MRI and CT modalities. We show that the proposed method markedly outperforms the default SAM 2, achieving average Dice Similarity Coefficient improvement of 0.14 and 0.11 in the scenarios when 5 volumes and 1 volume are available for the initial adaptation, respectively. SLM-SAM 2 also exhibits stronger resistance to over-propagation, making a notable step toward more accurate automated annotation of medical images for segmentation model development.