Twellmann Thorsten, Meyer-Baese Anke, Lange Oliver, Foo Simon, Nattkemper Tim W
Department of Electrical and Computer Engineering, Florida State University, Tallahassee, Florida 32310-6046.
Eng Appl Artif Intell. 2008 Mar;21(2):129-140. doi: 10.1016/j.engappai.2007.04.005.
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.
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，但对多时间点三维图像数据的评估给人类观察者带来了新的挑战。为辅助图像分析过程，我们应用监督和非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图像。这些技术是未来先进的计算机辅助诊断（CAD）系统的重要组成部分，并支持对确诊病变患者的DCE-MRI数据的空间和时间特征进行可视化探索。通过考虑癌组织的异质性，这些技术可揭示具有恶性、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亚分类，这是图像数据伪彩色呈现的基础。智能医疗系统有望通过无创成像对不确定乳腺病变的诊断做出贡献，从而在医疗保健政策方面产生重大影响。
