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

• URL: http://arxiv.org/abs/2411.00326v1
• Date: Fri, 01 Nov 2024 02:51:21 GMT
• Title: SpineFM: Leveraging Foundation Models for Automatic Spine X-ray Segmentation
• Authors: Samuel J. Simons, Bartłomiej W. Papież,
• Abstract summary: This paper introduces SpineFM, a novel pipeline that achieves state-of-the-art performance in the automatic segmentation and identification of vertebral bodies. We achieved outstanding results on two publicly available spine X-Ray datasets, with successful identification of 97.8% and 99.6% of annotated vertebrae.
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• Abstract: 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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