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Model–Free Visualization of Suspicious Lesions in Breast MRI Based on Supervised and Unsupervised Learning

机译：基于有监督和无监督学习的乳房MRI可疑病变的无模型可视化

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摘要

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.

机译：动态对比增强磁共振成像（DCE-MRI）已成为乳腺癌诊断中的重要工具，但是对多时间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，我们应用了有监督和无监督的模式识别技术来计算乳房MRI数据中可疑病变的增强可视化效果。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，并支持对病变诊断明确的患者DCE-MRI数据的时空特征进行可视化探索。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分类，这是图像数据伪彩色表示的基础。智能医疗系统有望通过无创成像有助于诊断不确定的乳腺病变，从而对医疗保健政治产生重大影响。

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期刊名称 other
作者
Thorsten Twellmann; Anke Meyer-Baese; Oliver Lange; Simon Foo; Tim W. Nattkemper;
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作者单位

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年(卷),期 -1(21),2
年度 -1
页码 129–140
总页数 26
原文格式 PDF
正文语种
中图分类
关键词
Classification clustering computer–aided diagnosis magnetic resonance imaging breast;

机译：分类;聚类;计算机辅助诊断;磁共振成像;乳腺;

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专利

1. Model-free visualization of suspicious lesions in breast MRI based on supervised and unsupervised learning [J] . Thorsten Twellmann, Anke Meyer-Baese, Oliver Lange, Engineering Applications of Artificial Intelligence . 2008,第2期

机译：基于监督和无监督学习的乳房MRI可疑病变的无模型可视化
2. Prediction of malignancy by a radiomic signature from contrast agent‐free diffusion MRI in suspicious breast lesions found on screening mammography. [J] . Bickelhaupt Sebastian, Paech Daniel, Kickingereder Philipp, Journal of magnetic resonance imaging: JMRI . 2017,第2期

机译：从筛选乳房摄影中发现的可疑乳腺病变中的造影剂无菌扩散MRI中的辐射素签名对恶性肿瘤预测。
3. MRI-guided needle localization of suspicious breast lesions: results of a freehand technique. [J] . van den Bosch MA, Daniel BL, Pal S, European radiology . 2006,第8期

机译：MRI引导下可疑乳腺病变的针头定位：徒手技术的结果。
4. Visualization of Suspicious Lesions in Breast MRI Based on Intelligent Neural Systems [C] . Thorsten Twellmann, Oliver Lange, Tim Wilhelm Nattkemper, Intelligent Computing: Theory and Applications IV . 2006

机译：基于智能神经系统的乳腺MRI可疑病变的可视化
5. Day surgery and recovery in women with a suspicious breast lesion: Evaluation of a psychoeducational nursing intervention. [D] . Allard, Nicole. 2005

机译：患有可疑乳腺病变的妇女的日间手术和康复：心理教育护理干预措施的评估。
6. Diagnosis of suspicious breast lesions using an empirical mathematical model for dynamic contrast-enhanced MRI [O] . Xiaobing Fan, Milica Medved, Gregory S. Karczmar, -1

机译：动态经验增强MRI的经验数学模型诊断可疑乳腺病变
7. Model–Free Visualization of Suspicious Lesions in Breast MRI Based on Supervised and Unsupervised Learning [O] . Twellmann, T Thorsten, Meyer-Bäse, A Anke, Lange, O, 2008

机译：基于有监督和无监督学习的乳房MRI可疑病变的无模型可视化

Model–Free Visualization of Suspicious Lesions in Breast MRI Based on Supervised and Unsupervised Learning

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