Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they do not exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of 60 annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter, thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented (\(p<0.05\)). This suggests that such advanced spatio-temporal characterization can improve the classification performance achieved in such small lesions, while simultaneously eliminating the need for precise segmentation.

中文翻译：

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动态乳腺 MRI 中小病灶的分类：通过随时间推移的对比度增强的时空分析消除对精确病灶分割的需要。

将乳房病变的尊严表征为良性或恶性对于小病变; 它们不表现出典型的恶性肿瘤特征，并且由于边缘难以可视化而难以分割。以前尝试使用动态或形态学标准对小病灶（平均病灶直径约 1 厘米）进行分类的尝试没有产生令人满意的结果。这项工作的目标是提高此类具有诊断挑战性的小病灶的分类性能，同时消除对精确病灶分割的需要。为此，我们介绍了一种随时间推移对病变增强模式进行拓扑表征的方法。从动态乳腺 MRI 检查中 60 个注释病变的所有五张对比后图像中提取了三个 Minkowski 泛函。对于每个闵可夫斯基泛函，从病灶的每个对比后图像中提取的拓扑特征组合成一个高维纹理特征向量。这些特征向量在支持向量回归的机器学习任务中被分类。为了进行比较，使用了源自灰度共生矩阵 (GLCM) 的传统 Haralick 纹理特征。这里还介绍和研究了一种提取阈值 GLCM 特征的新方法。使用 Minkowski Functionals 观察到最好的分类性能 这里还介绍和研究了一种提取阈值 GLCM 特征的新方法。使用 Minkowski Functionals 观察到最好的分类性能 这里还介绍和研究了一种提取阈值 GLCM 特征的新方法。使用 Minkowski Functionals 观察到最好的分类性能面积和周长，阈值 GLCM 特征 f8 和 f9，以及常规 GLCM 特征 f4 和 f6。然而，Minkowski Functionals 和阈值 GLCM 都在没有病灶分割的情况下取得了这样的结果，而当病灶没有被分割时，GLCM 特征的性能显着恶化（\（p<0.05\））。这表明这种先进的时空表征可以提高在如此小的病变中实现的分类性能，同时消除对精确分割的需要。