Breast perfusion data are dynamic medical image data that depict perfusion characteristics of the investigated tissue. These data consist of a series of static datasets that are acquired at different time points and aggregated into time intensity curves (TICs) for each voxel. The characteristics of these TICs provide important information about a lesion's composition, but their analysis is time‐consuming due to their large number. Subsequently, these TICs are used to classify a lesion as benign or malignant. This lesion scoring is commonly done manually by physicians and may therefore be subject to bias. We propose an approach that addresses both of these problems by combining an automated lesion classification with a visual confirmatory analysis, especially for uncertain cases. Firstly, we cluster the TICs of a lesion using ordering points to identify the clustering structure (OPTICS) and then visualize these clusters. Together with their relative size, they are added to a library. We then model fuzzy inference rules by using the lesion's TIC clusters as antecedents and its score as consequent. Using a fuzzy scoring system, we can suggest a score for a new lesion. Secondly, to allow physicians to confirm the suggestion in uncertain cases, we display the TIC clusters together with their spatial distribution and allow them to compare two lesions side by side. With our knowledge‐assisted comparative visual analysis, physicians can explore and classify breast lesions. The true positive prediction accuracy of our scoring system achieved 71.4 % in one‐fold cross‐validation using 14 lesions.

中文翻译：

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使用动态对比增强磁共振成像对乳腺癌进行知识辅助比较评估

乳房灌注数据是动态医学图像数据，描绘了被调查组织的灌注特性。这些数据由一系列静态数据集组成，这些数据集在不同时间点获取并聚合为每个体素的时间强度曲线 (TIC)。这些 TIC 的特征提供了有关病变组成的重要信息，但由于数量众多，它们的分析非常耗时。随后，这些 TIC 用于将病变分类为良性或恶性。这种病灶评分通常由医生手动完成，因此可能存在偏差。我们提出了一种通过将自动病变分类与视觉确认分析相结合来解决这两个问题的方法，特别是对于不确定的病例。首先，我们使用排序点对病变的 TIC 进行聚类以识别聚类结构 (OPTICS)，然后将这些聚类可视化。它们连同它们的相对大小一起被添加到一个库中。然后，我们通过使用病变的 TIC 簇作为前因并将其分数作为结果来对模糊推理规则进行建模。使用模糊评分系统，我们可以为新病灶建议评分。其次，为了让医生在不确定的情况下确认建议，我们将 TIC 集群与其空间分布一起显示，并允许他们并排比较两个病变。通过我们的知识辅助比较视觉分析，医生可以探索和分类乳房病变。我们的评分系统的真阳性预测准确率在使用 14 个病灶的单倍交叉验证中达到了 71.4%。