Automatic breast tissue segmentation in MRIs with morphology snake and deep denoiser training via extended Stein’s unbiased risk estimator

• DOI: 10.1007/s13755-021-00143-x
• Publication Date: 2021-04-05T17:02:55Z
• Authors: Xiao-Xia Yin; Yunxiang Jian; Yang Zhang; Yanchun Zhang; Jianlin Wu; Hui Lu; Min-Ying Su
• Keywords: 610; Applied Computing; Bioengineering; 02 engineering and technology; s unbiased risk estimator; 03 medical and health sciences; Breast cancer; 0302 clinical medicine; Information and Computing Sciences; Health Services and Systems; 616; Health Sciences; Breast Cancer; Health services and systems; 0202 electrical engineering, electronic engineering, information engineering; Adaptive histogram equalization; Extended Stein’ Cancer; Extended Stein’s unbiased risk estimator; Image segmentation; Applied computing; Inverse Gaussian gradient; 4.1 Discovery and preclinical testing of markers and technologies; Women's Health; Biomedical Imaging; Morphology snakes; MRI

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.