Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom.
PLoS One. 2019 Feb 14;14(2):e0212272. doi: 10.1371/journal.pone.0212272. eCollection 2019.
Aortic distensibility can be calculated using semi-automated methods to segment the aortic lumen on cine CMR (Cardiovascular Magnetic Resonance) images. However, these methods require visual quality control and manual localization of the region of interest (ROI) of ascending (AA) and proximal descending (PDA) aorta, which limit the analysis in large-scale population-based studies. Using 5100 scans from UK Biobank, this study sought to develop and validate a fully automated method to 1) detect and locate the ROIs of AA and PDA, and 2) provide a quality control mechanism.
The automated AA and PDA detection-localization algorithm followed these steps: 1) foreground segmentation; 2) detection of candidate ROIs by Circular Hough Transform (CHT); 3) spatial, histogram and shape feature extraction for candidate ROIs; 4) AA and PDA detection using Random Forest (RF); 5) quality control based on RF detection probability. To provide the ground truth, overall image quality (IQ = 0-3 from poor to good) and aortic locations were visually assessed by 13 observers. The automated algorithm was trained on 1200 scans and Dice Similarity Coefficient (DSC) was used to calculate the agreement between ground truth and automatically detected ROIs.
The automated algorithm was tested on 3900 scans. Detection accuracy was 99.4% for AA and 99.8% for PDA. Aorta localization showed excellent agreement with the ground truth, with DSC ≥ 0.9 in 94.8% of AA (DSC = 0.97 ± 0.04) and 99.5% of PDA cases (DSC = 0.98 ± 0.03). AA×PDA detection probabilities could discriminate scans with IQ ≥ 1 from those severely corrupted by artefacts (AUC = 90.6%). If scans with detection probability < 0.75 were excluded (350 scans), the algorithm was able to correctly detect and localize AA and PDA in all the remaining 3550 scans (100% accuracy).
The proposed method for automated AA and PDA localization was extremely accurate and the automatically derived detection probabilities provided a robust mechanism to detect low quality scans for further human review. Applying the proposed localization and quality control techniques promises at least a ten-fold reduction in human involvement without sacrificing any accuracy.
在电影心血管磁共振（Cardiovascular Magnetic Resonance，CMR）图像上使用半自动方法计算主动脉顺应性，可以对主动脉管腔进行分割。然而，这些方法需要视觉质量控制和手动定位升主动脉（AA）和近端降主动脉（PDA）的感兴趣区域（ROI），这限制了大规模基于人群的研究中的分析。本研究使用来自英国生物库的 5100 个扫描，旨在开发和验证一种全自动方法，用于 1）检测和定位 AA 和 PDA 的 ROI，以及 2）提供质量控制机制。
自动 AA 和 PDA 检测-定位算法遵循以下步骤：1）前景分割；2）通过圆形霍夫变换（CHT）检测候选 ROI；3）对候选 ROI 进行空间、直方图和形状特征提取；4）使用随机森林（RF）检测 AA 和 PDA；5）基于 RF 检测概率的质量控制。为了提供真实值，由 13 名观察者对整体图像质量（从差到好，IQ = 0-3）和主动脉位置进行视觉评估。自动算法在 1200 个扫描上进行了训练，并使用 Dice 相似系数（DSC）来计算真实值和自动检测 ROI 之间的一致性。
该自动算法在 3900 个扫描上进行了测试。AA 的检测准确率为 99.4%，PDA 的检测准确率为 99.8%。主动脉定位与真实值具有极好的一致性，AA 中 94.8%的病例（DSC = 0.97 ± 0.04）和 PDA 中 99.5%的病例（DSC = 0.98 ± 0.03）的 DSC ≥ 0.9。AA×PDA 的检测概率可以区分 IQ≥1 的扫描与严重受伪影影响的扫描（AUC = 90.6%）。如果排除检测概率<0.75 的扫描（350 个），该算法能够正确检测和定位所有剩余的 3550 个扫描中的 AA 和 PDA（准确率为 100%）。
所提出的自动 AA 和 PDA 定位方法非常准确，自动生成的检测概率提供了一种强大的机制，可用于检测低质量的扫描并进行进一步的人工审查。应用所提出的定位和质量控制技术有望将人工参与度降低至少十倍，而不会牺牲任何准确性。
