用基础模型实现脊柱X光片椎体自动分割，精度超现有方法。
SpineFM: Leveraging Foundation Models for Automatic Spine X-ray Segmentation
- 基于脊柱规律几何结构，逐个推断椎体位置。
- 在两个数据集上椎体识别率分别达97.8%和99.6%，平均Dice达0.942和0.921。
- 适合医学影像自动化分析研究人员参考。
本文提出SpineFM，一种新型管道，在颈椎与腰椎X光片中实现了椎体自动分割与识别的最先进性能。该方法利用脊柱的规律几何结构，采用新颖的归纳过程，沿脊柱序列推断每个椎体的位置。椎体分割采用Medical-SAM-Adaptor这一鲁棒的基础模型，区别于常用的基于CNN的模型。在两个公开脊柱X光数据集上取得优异结果，成功识别率分别为97.8%和99.6%；分割平均Dice值达到0.942和0.921，超越此前最优方法。
原文摘要 · Abstract (English)
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.
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