论文标题
在带有嘈杂标签的图像分割上：最佳解决方案的表征和体积特性
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice
论文作者
论文摘要
当目标标签嘈杂时，我们研究了医学图像分割，准确性和骰子中最流行的两个性能指标。对于这两个指标，都证明了与最佳分割集的表征和音量属性有关的几个语句，并提供了相关的实验。我们的主要见解是：（i）两个指标的解决方案的数量可能与目标的预期体积显着偏离，（ii）准确度溶液的体积始终小于或等于或等于（iii）在可行的嵌段时，这些度量的最佳解决方案与（iii）相同的量度相同的范围是相等的范围。
We study two of the most popular performance metrics in medical image segmentation, Accuracy and Dice, when the target labels are noisy. For both metrics, several statements related to characterization and volume properties of the set of optimal segmentations are proved, and associated experiments are provided. Our main insights are: (i) the volume of the solutions to both metrics may deviate significantly from the expected volume of the target, (ii) the volume of a solution to Accuracy is always less than or equal to the volume of a solution to Dice and (iii) the optimal solutions to both of these metrics coincide when the set of feasible segmentations is constrained to the set of segmentations with the volume equal to the expected volume of the target.
