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Abstract：
Evaluation of segmentation algorithms usually involves comparisons of segmentations to gold-standard delineations without regard to the ultimate medical decision-making task. We compare two segmentation evaluations methods-a Dice similarity coefficient (DSC) evaluation and a diagnostic classification task-based evaluation method using lesions from breast computed tomography. In our investigation, we use results from two previously developed lesion-segmentation algorithms [a global active contour model (GAC) and a global with local aspects active contour model]. Although similar DSC values were obtained (0.80 versus 0.77), we show that the global + local active contour (GLAC) model, as compared with the GAC model, is able to yield significantly improved classification performance in terms of area under the receivers operating characteristic (ROC) curve in the task of distinguishing malignant from benign lesions. [Area under the [Formula: see text] compared to 0.63, [Formula: see text]]. This is mainly because the GLAC model yields better detailed information required in the calculation of morphological features. Based on our findings, we conclude that the DSC metric alone is not sufficient for evaluating segmentation lesions in computer-aided diagnosis tasks.
摘要：
分割算法的评估通常涉及将分割与黄金标准轮廓进行比较，而不考虑最终的医疗决策任务。我们比较了两种分割评估方法-Dice相似性系数（DSC）评估和使用乳腺计算机断层扫描病变的基于诊断分类任务的评估方法。在我们的调查中,我们使用两种先前开发的病变分割算法[一种是全局活动轮廓模型(GAC),另一种是具有局部方面的全局活动轮廓模型]的结果.尽管获得了相似的DSC值（0.80对0.77），我们证明了全局+局部活动轮廓(GLAC)模型，与广汽模型相比，在区分恶性和良性病变的任务中，能够在接收器操作特征（ROC）曲线下的面积方面产生显着改善的分类性能。[[公式：见文本]下的面积与0.63相比，[公式：见文本]]。这主要是因为GLAC模型产生了形态学特征计算所需的更好的详细信息。根据我们的发现,我们得出的结论是，在计算机辅助诊断任务中，仅DSC指标不足以评估分割病变。
