Current medical image segmentation relies on the region-based (Dice, F1-score) and boundary-based (Hausdorff distance, surface distance) metrics as the de-facto standard. While these metrics are widely used, they lack a unified interpretation, particularly regarding volume agreement. Clinicians often lack clear benchmarks to gauge the "goodness" of segmentation results based on these metrics. Recognizing the clinical relevance of volumetry, we utilize relative volume prediction error (vpe) to directly assess the accuracy of volume predictions derived from segmentation tasks. Our work integrates theoretical analysis and empirical validation across diverse datasets. We delve into the often-ambiguous relationship between segmentation quality (measured by Dice) and volumetric accuracy in clinical practice. Our findings highlight the critical role of incorporating volumetric prediction accuracy into segmentation evaluation. This approach empowers clinicians with a more nuanced understanding of segmentation performance, ultimately improving the interpretation and utility of these metrics in real-world healthcare settings.

当前的医学图像分割依赖于基于区域（Dice，F1得分）和基于边界（Hausdorff距离，表面距离）的度量标准作为事实标准。本论文通过使用相对体积预测误差（vpe）来直接评估分割任务中体积预测的准确性，集成了理论分析和多样数据集的实证验证。研究发现，将体积预测准确性纳入分割评估中起到了关键作用，这使得临床医生在真实医疗场景中更准确理解分割性能，进而提升了这些度量标准的解释性和效用。

医学成像中的分割质量与体积准确性