Segmentation Quality and Volumetric Accuracy in Medical Imaging
arxiv(2024)
摘要
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.
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