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Imaging Biomarker Analysis of Rat Mammary Fat Pads and Glandular Tissues in MRI Images

机译:大鼠乳腺脂肪垫和MRI图像腺组织的成像分析

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In studying the relationship between risk factors and breast cancer, the growth patterns of fat pads and glandular tissues are considered as important biomarkers. The aim of this study is to measure the growth pattern statistics of rat mammary pads and glandular tissues with magnetic resonance (MR) time sequence images. In this paper, we proposed methods containing sequential steps to extract and analyze imaging biomarkers of rat mammary pad and glandular tissues. Firstly, to accurately segment out pads in MR images with noisy bias filed, we proposed a level set method combining local binary fitting (LBF) and geodesic active contour (GAC). The salient glandular tissue regions within the fat pads are further extracted by a scale-space analysis procedure. Then, the volume data of a single rat at different time points are aligned through profile correlation analysis. Finally, the growth rates are calculated and compared to show the changing patterns of fat pads and glandular tissues within separate groups. The experimental results showed the great utility of this approach in providing accurate measurements for novel risk factors of breast cancer.
机译:在研究风险因素和乳腺癌之间的关系时,脂肪垫和腺组织的生长模式被认为是重要的生物标志物。本研究的目的是测量大鼠乳腺垫和腺组织的生长模式统计,磁共振(MR)时间序列图像。在本文中,我们提出了含有顺序步骤的方法,以提取和分析大鼠乳腺垫和腺组织的成像生物标志物。首先,为了准确地分割出垫在MR图像与日提交的嘈杂偏压,我们提出了一个水平集方法结合局部二元接头(LBF)和测地活动轮廓(GAC)。脂肪垫内的凸起腺体组织区域进一步通过刻度空间分析程序提取。然后,通过轮廓相关分析对准不同时间点的单个RAT的体数据。最后,计算增长率并进行比较,以显示单独组内的脂肪垫和腺组织的变化模式。实验结果表明,这种方法在提供了准确测量乳腺癌的新危险因素方面的效用。

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