论文标题
CMR图像中具有里程碑意义检测的评估指标的比较
Comparison of Evaluation Metrics for Landmark Detection in CMR Images
论文作者
论文摘要
心脏磁共振（CMR）图像被广泛用于心脏诊断和心室评估。提取特定的地标（例如右心插入点）对于空间比对和3D建模至关重要。使用深度学习的多个小组已经解决了对此类地标的自动检测，但是对该领域评估指标的失败情况的关注很少。在这项工作中，我们用正确的心室插入点的其他标签扩展了公共ACDC数据集，并比较了基于热图的地标检测管道的不同变体。在此比较中，我们证明了明显简单的检测和本地化指标的陷阱，这些指标突出了清晰检测策略的重要性以及对基于本地化的指标的上限的定义。我们的初步结果表明，不同指标的组合是必要的，因为它们产生了不同的赢家进行方法比较。此外，它们强调了需要全面的度量描述和评估标准化的需要，尤其是对于无法计算指标或不存在指标的下限/上边界的错误情况。代码和标签：https：//github.com/cardio-ai/rvip_landmark_detection
Cardiac Magnetic Resonance (CMR) images are widely used for cardiac diagnosis and ventricular assessment. Extracting specific landmarks like the right ventricular insertion points is of importance for spatial alignment and 3D modeling. The automatic detection of such landmarks has been tackled by multiple groups using Deep Learning, but relatively little attention has been paid to the failure cases of evaluation metrics in this field. In this work, we extended the public ACDC dataset with additional labels of the right ventricular insertion points and compare different variants of a heatmap-based landmark detection pipeline. In this comparison, we demonstrate very likely pitfalls of apparently simple detection and localisation metrics which highlights the importance of a clear detection strategy and the definition of an upper limit for localisation-based metrics. Our preliminary results indicate that a combination of different metrics is necessary, as they yield different winners for method comparison. Additionally, they highlight the need of a comprehensive metric description and evaluation standardisation, especially for the error cases where no metrics could be computed or where no lower/upper boundary of a metric exists. Code and labels: https://github.com/Cardio-AI/rvip_landmark_detection
