使用变压器和基于GAN的图像恢复来提高牙咬牙的诊断质量
Mostafa Abtahi1, Sara Majidinia2, Meisam Mansourzadeh3
1Dental Material Research Center, School of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran.
Oral radiology
|February 4, 2026
概括
斯威尼尔深度学习最好地重建了牙科诊断的损坏的咬翼放射图. 这种方法比Real-ESRGAN更好地保护关键结构,提高了牙科诊断的准确性.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 咬伤性X射线图对于检测近距离虫和评估膜骨水平至关重要.
- 图像压缩和传感器的局限性会降低射线图的质量,影响诊断价值.
研究的目的:
- 评估两个深度学习方法的有效性,Real-ESRGAN和SwinIR,在重建细节从损坏的咬翼放射图片.
- 为了比较这些先进的人工智能模型恢复的图像的诊断实用性.
主要方法:
- 采用了4004张高质量的咬翅射线图的数据集,创建了配对的低分辨率和高分辨率图像.
- 真实ESRGAN和SwinIR模型进行了微调,并用于重建损坏的图像.
- 图像质量被一个由牙医组成的小组定量 (PSNR,SSIM) 和定性评估.
主要成果:
- 与Real-ESRGAN (PSNR: 31.93 dB, SSIM: 0.8227) 相比,SwinIR获得了更高的定量得分 (PSNR: 35.56 dB,SSIM: 0.9287).与实体ESRGAN相比,SwinIR获得了更高的定量得分 (PSNR: 31.56 dB,SSIM: 0.9287).与实体ESRGAN相比,SwinIR获得了更高的定量得分.
- 牙医更喜欢SwinIR,因为它可以保存诊断结构,如牙边缘和椎图案.
- 真实-ESRGAN以感知现实主义而闻名,但对诊断细节的保存却不那么着名.
结论:
- 斯威尼尔IR模型在咬伤翼放射图片中保持临床显著结构方面表现出卓越的性能.
- 当诊断精度至关重要时,SwinIR是首选的深度学习方法,用于增强咬伤翼放射图像.
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