Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important tool in breast cancer diagnosis, but evaluation of multitemporal 3D image data holds new challenges for human observers. To aid the image analysis process, we apply supervised and unsupervised pattern recognition techniques for computing enhanced visualizations of suspicious lesions in breast MRI data. These techniques represent an important component of future sophisticated computer-aided diagnosis (CAD) systems and support the visual exploration of spatial and temporal features of DCE-MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogeneity of cancerous tissue, these techniques reveal signals with malignant, benign and normal kinetics. They also provide a regional subclassification of pathological breast tissue, which is the basis for pseudo-color presentations of the image data. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.

中文翻译：

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基于监督和无监督学习的乳腺 MRI 可疑病变的无模型可视化。

动态对比增强磁共振成像 (DCE-MRI) 已成为乳腺癌诊断的重要工具，但对多时相 3D 图像数据的评估对人类观察者提出了新的挑战。为了帮助图像分析过程，我们应用有监督和无监督模式识别技术来计算乳腺 MRI 数据中可疑病变的增强可视化。这些技术代表了未来复杂的计算机辅助诊断 (CAD) 系统的重要组成部分，并支持对来自确诊病灶诊断患者的 DCE-MRI 数据的空间和时间特征的视觉探索。通过考虑癌组织的异质性，这些技术揭示了具有恶性、良性和正常动力学的信号。它们还提供了病理性乳腺组织的区域子分类，这是图像数据伪彩色显示的基础。智能医疗系统有望通过非侵入性成像对不确定的乳房病变进行诊断，从而对医疗保健政治产生重大影响。