零射击脊椎实例细分在DICOM脊柱X射线图上使用快速细分任何模型
Alexander Sieradzki1, Kamil Koszela2, Szymon Koszykowski1
1Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, ul. Nowoursynowska 159, 02-776 Warsaw, Poland.
Journal of clinical medicine
|March 14, 2026
概括
快速的基础模型可以在脊椎放射图上执行零射击脊椎实例细分. 矩形提示允许准确的细分,没有任务特定的培训,优于点提示.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 准确的脊椎实例细分对于脊柱参数评估至关重要.
- 对细分的监督方法需要广泛,昂贵的注释,并且可以与域名转移作斗争.
- 探索自动化细分方法对于高效的临床工作流程至关重要.
研究的目的:
- 在原始DICOM全脊椎放射图上评估提示性细分基础模型的零射击概括能力.
- 为了比较不同模型 (SAM-ViT-Huge,SAM2-Hiera-Large,MedSAM-ViT-Base) 和提示策略 (点与矩形) 的性能.
- 确定使用这些模型而不需要特定任务微调的可行性.
主要方法:
- 评估了SAM-ViT-Huge,SAM2-Hiera-Large和MedSAM-ViT-Base在144张全脊椎放射图和1309张脊椎口罩上进行了评估.
- 使用标准化管道进行DICOM解码,强度正常化和自动提示生成.
- 使用Oracle和模型得分协议评估性能,测量IOU,Dice,精度,回忆,ASSD和HD95.
主要成果:
- 使用矩形提示的SAM-ViT-Huge实现了最高的平均IoU/Dice (0.782/0.870预言; 0.737/0.837模型得分).
- SAM2-Hiera-Large也在矩形提示方面表现良好,表现优于MedSAM-ViT-Base.
- 点提示导致重叠明显减少,边界误差更高,尽管回忆率很高.
结论:
- 在原始的DICOM脊柱放射图上,零射击脊椎实例细分是可以使用快速基础模型实现的.
- 在这个任务中,矩形提示显然比点提示更有效.
- 基金会模型显示,它有望减少脊柱成像分析中的注释负担.
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