利用多探测器CT图像的2D重建标记椎骨：一种阐明脊柱解剖学先验知识的对抗性方法
Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy
摘要：
目的：使用并测试一种标记算法，该算法对二维（2D）重建而不是三维（3D）数据进行操作，以定位和识别椎骨。研究方法：我们改进了Btrfly Net（由Sekuboyina等人描述），该网络适用于矢状和冠状最大强度投影（MIP），并使用两个额外的组件对其进行了增强：脊柱定位和对抗优先学习。此外，我们探索了两种对抗性训练方案的变体，这些方案将解剖学先验知识纳入Btrfly Net。我们研究了所提出的方法在三个数据集上标记椎骨的优越性：302个CT扫描的公共基准数据集和两个内部数据集，共238个CT扫描。我们采用Wilcoxon符号秩检验来计算由于我们的方法中的各种架构组件而观察到的性能改善的统计显著性。结果如下：在公共数据集上，我们使用所描述的Btrfly（pe-eb）网络的方法与当前最先进的方法相当，实现了统计学上的显著性。（p <0.001）椎骨识别率为88.5+/-0.2%，定位距离小于7 mm。在内部数据集上，具有较高的扫描间数据变异性，我们获得了85.1 ± 1.2%的识别率。结论：当在2D MIP上标记椎骨时，实现了与现有3D方法相当的识别性能。使用所提出的对抗性训练制度，有效地加强了当地的脊椎先验知识在训练过程中的性能进一步提高。最后，脊柱本地化增加了我们的方法的通用性，在MIP的内容。
英文摘要：
Purpose: To use and test a labelling algorithm that operates on two-dimensional (2D) reformations, rather than three-dimensional (3D) data to locate and identify vertebrae. Methods: We improved the Btrfly Net (described by Sekuboyina et al) that works on sagittal and coronal maximum intensity projections (MIP) and augmented it with two additional components: spine-localization and adversarial a priori-learning. Furthermore, we explored two variants of adversarial training schemes that incorporated the anatomical a priori knowledge into the Btrfly Net. We investigated the superiority of the proposed approach for labelling vertebrae on three datasets: a public benchmarking dataset of 302 CT scans and two in-house datasets with a total of 238 CT scans. We employed Wilcoxon signed-rank test to compute the statistical significance of the improvement in performance observed due to various architectural components in our approach. Results: On the public dataset, our approach using the described Btrfly(pe-eb) network performed on par with current state-of-the-art methods achieving a statistically significant (p < .001) vertebrae identification rate of 88.5+/-0.2 % and localization distances of less than 7-mm. On the in-house datasets that had a higher inter-scan data variability, we obtained an identification rate of 85.1+/-1.2%. Conclusion: An identification performance comparable to existing 3D approaches was achieved when labelling vertebrae on 2D MIPs. The performance was further improved using the proposed adversarial training regime that effectively enforced local spine a priori knowledge during training. Lastly, spine-localization increased the generalizability of our approach by homogenizing the content in the MIPs.
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