Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of these models in order for results to be comparable. However, there is a mismatch between the distributions of cases and difficulty level of segmentation tasks in public data sets compared to clinical practice. Common metrics fail to measure the impact of this mismatch, especially for clinical data sets that include low signal pathologies, a difficult segmentation task, and uncertain, small, or empty reference annotations. This limitation may result in ineffective research of machine learning practitioners in designing and optimizing models. Dimensions of evaluating clinical value include consideration of the uncertainty of reference annotations, independence from reference annotation volume size, and evaluation of classification of empty reference annotations. We study how uncertain, small, and empty reference annotations influence the value of metrics for medical image segmentation on an in-house data set regardless of the model. We examine metrics behavior on the predictions of a standard deep learning framework in order to identify metrics with clinical value. We compare to a public benchmark data set (BraTS 2019) with a high-signal pathology and certain, larger, and no empty reference annotations. We may show machine learning practitioners, how uncertain, small, or empty reference annotations require a rethinking of the evaluation and optimizing procedures. The evaluation code was released to encourage further analysis of this topic. https://github.com/SophieOstmeier/UncertainSmallEmpty.git

翻译：医学图像分割模型的性能指标用于衡量参考标注与预测分割之间的一致性。通常，重叠指标（如Dice系数）被用作评估这些模型性能的指标，以便结果具有可比性。然而，在公开数据集与临床实践中，病例分布和分割任务难度之间存在不匹配。常见指标无法衡量这种不匹配的影响，尤其是对于包含低信号病理、困难分割任务以及不确定、微小或空参考标注的临床数据集。这一局限性可能导致机器学习从业者在设计和优化模型时的研究效率低下。评估临床价值的维度包括：考虑参考标注的不确定性、独立于参考标注体积大小，以及评估空参考标注的分类。我们在内部数据集上研究了不确定、微小和空参考标注如何影响医学图像分割指标的值（而与模型无关）。我们检验了标准深度学习框架预测结果中指标的行为，以识别具有临床价值的指标。我们将其与高信号病理、明确、较大且无空参考标注的公开基准数据集（BraTS 2019）进行了比较。我们向机器学习从业者展示：不确定、微小或空参考标注需要重新审视评估和优化流程。评估代码已公开发布，以促进对该主题的进一步分析。https://github.com/SophieOstmeier/UncertainSmallEmpty.git