BACKGROUND The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to automatically detect when a segmentation method fails in order to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. METHODS To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4800 cardiovascular magnetic resonance (CMR) scans. We then apply our method to a large cohort of 7250 CMR on which we have performed manual QC. RESULTS We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using the predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4800 scans for which manual segmentations were available. We mimic real-world application of the method on 7250 CMR where we show good agreement between predicted quality metrics and manual visual QC scores. CONCLUSIONS We show that Reverse classification accuracy has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

中文翻译：

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图像分割中的自动质量控制：在英国生物银行心血管磁共振成像研究中的应用。


背景技术包括群体成像在内的大规模研究的趋势在质量控制（QC）方面提出了新的挑战。当采用图像分割方法等自动处理工具来导出定量测量或生物标记以进行进一步分析时，这是一个特殊的问题。每个分割结果的手动检查和视觉质量控制在大规模上是不可行的。然而，重要的是能够自动检测分割方法何时失败，以避免在后续分析中包含错误的测量结果，否则可能导致错误的结论。方法为了克服这一挑战，我们探索了一种基于反向分类准确性的预测分割质量的方法，这使我们能够根据案例区分成功和失败的分割。我们在一组新的、大规模手动注释的 4800 幅心血管磁共振 (CMR) 扫描中验证了这种方法。然后，我们将我们的方法应用于一大群 7250 CMR，我们已对其进行了手动 QC。结果我们报告了用于预测分割质量指标的结果，包括骰子相似系数（DSC）和表面距离测量。作为初步验证，我们提供了 400 次扫描的数据，证明使用预测的 DSC 分数对低质量和高质量分割进行分类的准确度为 99%。作为进一步验证，我们在 4800 次扫描（可进行手动分割）中显示出真实分数和预测分数之间的高度相关性以及 95% 的分类准确度。我们在 7250 CMR 上模拟了该方法的实际应用，其中预测的质量指标和手动视觉 QC 分数之间表现出良好的一致性。 结论 我们表明，在英国生物银行成像研究中的大规模群体成像背景下，反向分类准确性具有针对每个病例​​进行准确且全自动分割质量控制的潜力。