论文标题
迈向医学图像细分中评估指标的指南
Towards a Guideline for Evaluation Metrics in Medical Image Segmentation
论文作者
论文摘要
在过去的十年中，对人工智能的研究通过深度学习模型迅速增长，尤其是在医学图像分割领域。各种研究表明，这些模型具有强大的预测能力，并获得了与临床医生相似的结果。但是，最近的研究表明，图像分割研究中的评估缺乏可靠的模型绩效评估，并且通过不正确的度量实施或用法显示统计偏差。因此，这项工作为二进制和多类问题的以下指标提供了以下指标的概述和解释指南：骰子相似性系数，jaccard，jaccard，敏感性，特异性，RAND INDEX，ROC曲线，Cohen的Kappa和Hausdorff距离。总而言之，我们提出了标准化医学图像分割评估的指南，以提高研究领域的评估质量，可重复性和可比性。
In the last decade, research on artificial intelligence has seen rapid growth with deep learning models, especially in the field of medical image segmentation. Various studies demonstrated that these models have powerful prediction capabilities and achieved similar results as clinicians. However, recent studies revealed that the evaluation in image segmentation studies lacks reliable model performance assessment and showed statistical bias by incorrect metric implementation or usage. Thus, this work provides an overview and interpretation guide on the following metrics for medical image segmentation evaluation in binary as well as multi-class problems: Dice similarity coefficient, Jaccard, Sensitivity, Specificity, Rand index, ROC curves, Cohen's Kappa, and Hausdorff distance. As a summary, we propose a guideline for standardized medical image segmentation evaluation to improve evaluation quality, reproducibility, and comparability in the research field.
