Extended surface distance for local evaluation of 3D medical image segmentations

Abstract

The evaluation of 3D medical image segmentation quality requires a reliable detailed comparison of a reference segmentation with an automatic segmentation. It should be able to measure the quality accurately and, thus, to reveal problematic regions. While several (global) measures, providing a single quality value, are available, the only widely used local measure is the Surface Distance (i.e., point-to-surface distance). This measure, however, has significant drawbacks such as asymmetry and underestimation in distant and differently formed regions. Other available measures have limited suitability for 3D medical segmentation evaluation. We present a more reliable distance measure for assessing and analyzing local differences between automatic and reference (i.e., ground truth) 3D segmentations. We identify and overcome Surface Distance drawbacks, esp. in regions with larger dissimilarities. We evaluated our approach on four real medical image datasets. The results indicate that our measure provides more accurate local distance values

Editorial

“…The paper by Dou et al [21] is about non-rigid registration; the one by Wang et al [22] focuses on the problem of 3D object tracking in videos and the paper by Getto et al [23] is about medical image segmentation.…”