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    Breast Regions Segmentation Based on U-net++ from DCE-MRI Image Sequences

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    Author
    Sui, Dong
    Huang, Zixuan
    Song, Xinwei
    Zhang, Yue
    Wang, Yantao
    Zhang, Lei
    Date
    2021-01-27
    Journal
    Journal of Physics: Conference Series
    Publisher
    IOP Publishing Ltd
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1088/1742-6596/1748/4/042058
    Abstract
    Background analysis of breast cancer can depict the progress and states of the tumour, which is based on the whole breast segmentation from MRI images. The focus of this paper is to construct a pipeline for breast region segmentation for the possibility of breast cancer automatic diagnosis by using MRI image serials. Studies of breast region segmentation based on traditional and deep learning methods have undergone several years, but most of them have not achieved a satisfactory consequence for the following background analysis. In this paper, we proposed a novel pipeline for whole breast region segmentation method based on U-net++, that can achieve a better result compared with the traditional U-net model which is the most common used medical image analysis model and achieve a better IoU than CNN models. We have evaluated the U-net++ model with tradition U-net, our experiments demonstrate that the U-net++ with deep supervision achieves a higher IoU over U-net model.
    Keyword
    breast region segmentation
    DCE-MRI
    U-net++
    Breast--Cancer
    Diagnostic Imaging--methods
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/15027
    ae974a485f413a2113503eed53cd6c53
    10.1088/1742-6596/1748/4/042058
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