论文标题
恶作剧：用于评估弱标记数据的医学图像分割度量
MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled Data
论文作者
论文摘要
绩效指标是评估和比较不同医学图像分割算法的重要工具。不幸的是，当涉及评估某些边缘案例时，当前的措施有弱点。当评估具有很小关注区域或根本没有关注区域的图像时，这些局限性就会引起。作为解决这些局限性的解决方案，我们提出了一个新的医疗图像分割度量：恶作剧。为了评估失败者，使用磁铁共振层析成像图像从几种情况下比较了医学图像分割和不为人知的流行指标。为了允许在社区中应用和实验结果的可重复性，我们在公共可用的评估框架中包括了失败：
Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms. Unfortunately, the current measures have their weaknesses when it comes to assessing certain edge cases. These limitations arouse when images with a very small region of interest or without a region of interest at all are assessed. As a solution for these limitations, we propose a new medical image segmentation metric: MISm. To evaluate MISm, the popular metrics in the medical image segmentation and MISm were compared using images of magnet resonance tomography from several scenarios. In order to allow application in the community and reproducibility of experimental results, we included MISm in the publicly available evaluation framework MISeval: https://github.com/frankkramer-lab/miseval/tree/master/miseval
