Machine learning-based segmentation in medical imaging is widely used in clinical applications from diagnostics to radiotherapy treatment planning. Segmented medical images with ground truth are useful for investigating the properties of different segmentation performance metrics to inform metric selection. Regular geometrical shapes are often used to synthesize segmentation errors and illustrate properties of performance metrics, but they lack the complexity of anatomical variations in real images. In this study, we present a tool to emulate segmentations by adjusting the reference (truth) masks of anatomical objects extracted from real medical images. Our tool is designed to modify the defined truth contours and emulate different types of segmentation errors with a set of user-configurable parameters. We defined the ground truth objects from 230 patient images in the Glioma Image Segmentation for Radiotherapy (GLIS-RT) database. For each object, we used our segmentation synthesis tool to synthesize 10 versions of segmentation (i.e., 10 simulated segmentors or algorithms), where each version has a pre-defined combination of segmentation errors. We then applied 20 performance metrics to evaluate all synthetic segmentations. We demonstrated the properties of these metrics, including their ability to capture specific types of segmentation errors. By analyzing the intrinsic properties of these metrics and categorizing the segmentation errors, we are working toward the goal of developing a decision-tree tool for assisting in the selection of segmentation performance metrics.
翻译:在临床应用中,从诊断到放射疗法治疗规划,医疗成像中的机器学习分解广泛用于临床应用,从诊断到放射治疗治疗规划。具有地面真相的分解医疗图象有助于调查不同分解性性业绩指标的特性,为衡量标准选择提供信息。经常使用定期几何形状来合成分解错误,并展示性能指标的特性,但是它们缺乏真实图像中解剖变异的复杂性。在本研究中,我们提出了一个工具,通过调整从真实医学图像中提取的解剖性物体的参考(真相)遮罩来模拟分解。我们的工具旨在修改定义的真相轮廓,并用一套用户可配置参数来模拟不同类型的分解错误。我们从Glioma图像分解(GLIS-RT)数据库中的230个病人图像中定义了地面真相目标对象。我们使用分解综合工具来合成10种分解(即10种模拟分解或算法),每个版本都有预先定义的组合错误。我们随后应用20种性衡量指标来评估所有合成分解的分解性误差,包括精确分解能力的分解能力。我们对这些分解性指标的分解的分解特性的分解特性进行了分析,我们对这些分解性指标的分解性分析这些分解的特性的分解特性的分解特性的分解,我们对这些分解的分解特性进行了了这些分解的分解的分解的分解的分解的分解特性的分解方法的分解方法的分解能力。我们算。