Yin Xiao-Xia, Jian Yunxiang, Zhang Yang, Zhang Yanchun, Wu Jianlin, Lu Hui, Su Min-Ying
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China.
Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA USA.
Health Inf Sci Syst. 2021 Apr 5;9(1):16. doi: 10.1007/s13755-021-00143-x. eCollection 2021 Dec.
Accurate segmentation of the breast tissue is a significant challenge in the analysis of breast MR images, especially analysis of breast images with low contrast. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI). Especially, when tumour is imaged for scanning, how to isolate the tumour region from chest will directly affect the accuracy for tumour to be detected. Due to low intensity levels and the close connection between breast and chest portion in MRIs, this study proposes an innovative, fully automatic and fast segmentation approach which combines histogram with inverse Gaussian gradient for morphology snakes, along with extended Stein's unbiased risk estimator (eSURE) applied for unsupervised learning of deep neural network Gaussian denoisers, aimed at accurate identification of landmarks such as chest and breast.
在乳腺磁共振成像分析中，尤其是对低对比度乳腺图像的分析，准确分割乳腺组织是一项重大挑战。现有的大多数乳腺分割方法都是半自动的，在获得准确结果的能力方面存在局限。这是因为从有噪声的磁共振图像（MRI）中去除标记点存在困难。特别是，当对肿瘤进行扫描成像时，如何将肿瘤区域与胸部隔离开来将直接影响肿瘤检测的准确性。由于MRI中强度水平较低以及乳腺与胸部部分之间的紧密连接，本研究提出了一种创新的、全自动且快速的分割方法，该方法将直方图与用于形态学蛇形模型的逆高斯梯度相结合，同时将扩展的斯坦无偏风险估计器（eSURE）应用于深度神经网络高斯去噪器的无监督学习，旨在准确识别胸部和乳腺等标记点。
