Fully multi-target segmentation for breast ultrasound image based on fully convolutional network.
Ultrasound image segmentation plays an important role in computer-aided diagnosis of breast cancer. Existing approaches focused on extracting the tumor tissue to characterize the tumor class. However, other tissues are also helpful for providing the references. In this paper, a multi-target semantic...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 9; pp. 2049 - 2062 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145048075&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145048075 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2020 vid: 58 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145048075 144447087 145048075 NLM32638276 10.1007/s11517-020-02200-1 NLM32638276 145048075 ppf: 2049 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Fully multi-target segmentation for breast ultrasound image based on fully convolutional network. aug: au: Zhang, Yingtao Liu, Yan Cheng, Hengda Li, Ziyao Liu, Cong affil: School of Computer Science and Technology, Harbin Institute of Technology, No. 92, Xidazhi Street, 150001, Harbin, China sug: subj: Breast Ultrasonography Methods Breast Neoplasms Ultrasonography Statistics and Numerical Data Logic Bioinformatics Female Image Interpretation, Computer Assisted Statistics and Numerical Data Resource Databases Image Interpretation, Computer Assisted Methods Female ab: Ultrasound image segmentation plays an important role in computer-aided diagnosis of breast cancer. Existing approaches focused on extracting the tumor tissue to characterize the tumor class. However, other tissues are also helpful for providing the references. In this paper, a multi-target semantic segmentation approach is proposed based on the fully convolutional network for segmenting the breast ultrasound image into different target tissue regions. For handling the uncertain affiliation of pixels in blurry boundaries, the certain outputs of pixel characteristics in AlexNet are transformed into the fuzzy decision expression. For improving the image detail representation, the AlexNet network structure of fully convolutional network is optimized with fully connected skip structure. In addition, the output of net model is optimized with fully connected conditional random field to improve the characterization of spatial consistency and pixels' correlation of the image. Moreover, a data training optimization method is developed for improving the efficiency of network training. In the experiment, 325 ultrasound images and four error metrics are utilized for validating the segmentation performance. Comparing with existing methods, experimental results show that the proposed approach is effective for handling the breast ultrasound images accurately and reliably. Graphical abstract. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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