Radiotherapy Department, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, 213000, China.
Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, 213000, China.
BMC Med Imaging. 2024 Aug 6;24(1):204. doi: 10.1186/s12880-024-01379-1.
Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals.
This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size.
A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation.
Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data.
DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.
计算机断层扫描（CT）在临床上广泛应用，但会受到金属植入物的影响。金属分割对于金属伪影校正至关重要，而常见的阈值方法往往无法准确分割金属。
本研究旨在使用扩散模型分割 CT 图像中的金属植入物，并使用临床伪影图像和已知大小的体模图像进行验证。
对 100 名未发生金属伪影的接受放射治疗的患者进行回顾性研究，使用公开的掩模数据生成模拟伪影数据。研究使用 11280 个切片进行训练和验证，2820 个切片进行测试。使用 DiffSeg 进行金属掩模分割，DiffSeg 是一种包含条件动态编码和全局频率解析器（GFParser）的扩散模型。条件动态编码融合了当前分割掩模和多个尺度的先前图像，而 GFParser 有助于消除掩模中的高频噪声。还使用临床伪影图像和体模图像进行模型验证。
与真实值相比，DiffSeg 对模拟数据的金属分割准确性为 97.89%，DSC 为 95.45%。基于 2500 HU 和 3000 HU 的阈值分割得到的掩模形状覆盖了真实值，阈值分割的 DSC 分别为 82.92%和 84.19%。评估指标和可视化结果表明，DiffSeg 比其他经典深度学习网络表现更好，尤其是对临床 CT、伪影数据和体模数据。
DiffSeg 利用条件动态编码和 GFParser 有效地、稳健地分割了伪影数据中的金属掩模。未来的工作将包括将金属分割模型嵌入到金属伪影减少中，以提高减少效果。
