通过使用深度学习,自动对增强骨进行细分,经过跨膜鼻腔地板升高后.
Kexin Yang1, Wenjun Duan1, Wangtao Lu2
1Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Zhejiang University School of Medicine, Cancer Center of Zhejiang University, Hangzhou, China.
International dental journal
|March 8, 2026
概括
深度学习精确地在跨膜鼻腔底部升高 (TSFE) 后细分增强骨. UNETR++模型显示出卓越的性能和效率,显著减少了测量时间.
科学领域:
- 生物医学工程 生物医学工程
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 穿膜鼻腔地面升高 (TSFE) 是一种常见的程序,以增加骨体积.
- 对增强骨的准确细分对于评估治疗结果至关重要.
- 手动细分是耗时的,容易引起观察者之间的变化.
研究的目的:
- 评估深度学习模型的性能,用于TSFE后增强骨的自动细分.
- 为了比较不同深度学习架构的准确性和效率.
主要方法:
- 对103名患者的圆束计算机断层扫描 (CBCT) 数据进行了回顾性分析.
- 四个深度学习模型的培训和验证:UNETR++,Swin Transformer,U-Net和3D-VNet.
- 性能评估使用子相似系数 (DSC),交叉点与联盟 (IoU),灵敏度,精度,豪斯多夫距离 (HD95) 和准确度.
主要成果:
- UNETR++实现了最高的性能,平均DSC为0.8477和IOU为0.7356. UNETR++实现了最高的性能,平均DSC为0.8477和IOU为0.7356.
- 与手动细分相比,UNETR++表现出极好的可重现性.
- 自动细分显著缩短了测量时间,达到14.96 ± 2.57秒.
结论:
- 深度学习模型,特别是UNETR++,为TSFE后的增强骨分割提供了准确有效的解决方案.
- 自动化细分有助于客观地评估骨增强和治疗成功.
相关概念视频
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