Objective

To design, develop and evaluate an automated multi-atlas method for segmentation and volume quantification of gluteus maximus from Dixon and T1-weighted images.

Materials and methods

The multi-atlas segmentation method uses an atlas library constructed from 15 Dixon MRI scans of healthy subjects. A non-rigid registration between each atlas and the target, followed by majority voting label fusion, is used in the segmentation. We propose a region of interest (ROI) to standardize the measurement of muscle bulk. The method was evaluated using the dice similarity coefficient (DSC) and the relative volume difference (RVD) as metrics, for Dixon and T1-weighted target images.

Results

The mean(± SD) DSC was 0.94 ± 0.01 for Dixon images, while 0.93 ± 0.02 for T1-weighted. The RVD between the automated and manual segmentation had a mean(± SD) value of 1.5 ± 4.3% for Dixon and 1.5 ± 4.8% for T1-weighted images. In the muscle bulk ROI, the DSC was 0.95 ± 0.01 and the RVD was 0.6 ± 3.8%.

Conclusion

The method allows an accurate fully automated segmentation of gluteus maximus for Dixon and T1-weighted images and provides a relatively accurate volume measurement in shorter times (~ 20 min) than the current gold-standard manual segmentations (2 h). Visual inspection of the segmentation would be required when higher accuracy is needed.

中文翻译：

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来自 Dixon 和 T1 加权磁共振图像的臀大肌自动多图谱分割。

客观的

设计、开发和评估一种自动多图谱方法，用于从 Dixon 和 T1 加权图像中对臀大肌进行分割和体积量化。

材料和方法

多图谱分割方法使用由健康受试者的 15 次 Dixon MRI 扫描构建的图谱库。在分割中使用每个图集和目标之间的非刚性配准，然后是多数投票标签融合。我们提出了一个感兴趣区域 (ROI) 来标准化肌肉体积的测量。对于 Dixon 和 T1 加权目标图像，该方法使用骰子相似系数 (DSC) 和相对体积差异 (RVD) 作为指标进行评估。

结果

Dixon 图像的平均值 (± SD) DSC 为 0.94 ± 0.01，而 T1 加权的平均值为 0.93 ± 0.02。自动和手动分割之间的 RVD 的平均 (± SD) 值为 Dixon 的 1.5 ± 4.3% 和 T1 加权图像的 1.5 ± 4.8%。在肌肉体积 ROI 中，DSC 为 0.95 ± 0.01，RVD 为 0.6 ± 3.8%。

结论

该方法允许对 Dixon 和 T1 加权图像的臀大肌进行准确的全自动分割，并在比当前的黄金标准手动分割（2 小时）更短的时间内（约 20 分钟）提供相对准确的体积测量。当需要更高的精度时，需要对分割进行目视检查。