标题： SpineFM：利用基础模型进行自动脊柱X射线分割 显示英文标题

标题： SpineFM: Leveraging Foundation Models for Automatic Spine X-ray Segmentation

摘要： 本文介绍了SpineFM，这是一种新颖的管道，在颈椎和腰椎X线影像的椎体自动分割和识别方面达到了最先进的性能。 SpineFM利用了脊柱的规则几何形状，采用了一种新的归纳过程，沿脊柱列顺序推断每个椎骨的位置。 椎骨使用Medical-SAM-Adaptor进行分割，这是一种与常用的基于CNN的模型不同的稳健基础模型。 我们在两个公开可用的脊柱X射线数据集上取得了出色的结果，分别成功识别了97.8%和99.6%的注释椎骨。 其中，我们的分割达到了平均Dice系数为0.942和0.921，超过了先前的最先进方法。 显示英文摘要

摘要： This paper introduces SpineFM, a novel pipeline that achieves state-of-the-art performance in the automatic segmentation and identification of vertebral bodies in cervical and lumbar spine radiographs. SpineFM leverages the regular geometry of the spine, employing a novel inductive process to sequentially infer the location of each vertebra along the spinal column. Vertebrae are segmented using Medical-SAM-Adaptor, a robust foundation model that diverges from commonly used CNN-based models. We achieved outstanding results on two publicly available spine X-Ray datasets, with successful identification of 97.8\% and 99.6\% of annotated vertebrae, respectively. Of which, our segmentation reached an average Dice of 0.942 and 0.921, surpassing previous state-of-the-art methods.