多级图像金字塔网络用于CBCT图像中关节的粗细细分段
Piaolin Hu1, Jupeng Li1, Ruohan Ma2
1School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
Biomedizinische Technik. Biomedical engineering
|November 25, 2025
概括
在束计算机断层扫描 (CBCT) 图像中精确细分关节 (TMJ),对于诊断关节骨关节炎至关重要. 一个新的多阶段图像金字塔网络 (MIP-Net) 在CBCT扫描中显示出高精度的TMJ结构细分.
科学领域:
- 医学成像分析分析 医学成像分析
- 放射学中的人工智能
- 形束计算断层扫描 (CBCT) 是一种
背景情况:
- 在CBCT图像中精确对关节 (TMJ) 的细分对于诊断关节骨关节炎 (TMJ-OA) 至关重要.
- 目前的细分方法在精确地划分TMJ结构,特别是状腺时可能面临挑战.
研究的目的:
- 开发和评估一个多阶段的图像金字塔网络 (MIP-Net),用于在CBCT图像中精确的TMJ细分.
- 评估MIP-Net在TMJ细分方面的表现,以改善TMJ-OA诊断.
主要方法:
- 提出了一个新的多阶段图像金字塔网络 (MIP-Net).
- 该网络使用逐步下降采样来创建一个图像金字塔.
- 特征融合集成了多尺度特征,以提高细分精度.
主要成果:
- 在MIP-Net实现了98.02%±0.65%的子相似系数 (DSC).
- 平均表面距离 (ASD) 是0.0605毫米±0.0229毫米.
- 豪斯多夫距离 (HD) 是0.1907毫米±0.0627毫米.
结论:
- 拟议的MIP-Net有效地在CBCT图像中执行精确的TMJ细分.
- 这种精确的细分有可能提高TMJ-OA的临床诊断和治疗.
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