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DBT Masses Automatic Segmentation Using U-Net Neural Networks
Joint Authors
Lai, Xiaobo
Yang, Weiji
Li, Ruipeng
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture.
Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image.
Secondly, an efficient U-Net neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the U-Net model.
And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels.
Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results.
The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images.
Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively.
Our proposed U-Net-based DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects.
American Psychological Association (APA)
Lai, Xiaobo& Yang, Weiji& Li, Ruipeng. 2020. DBT Masses Automatic Segmentation Using U-Net Neural Networks. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1139547
Modern Language Association (MLA)
Lai, Xiaobo…[et al.]. DBT Masses Automatic Segmentation Using U-Net Neural Networks. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1139547
American Medical Association (AMA)
Lai, Xiaobo& Yang, Weiji& Li, Ruipeng. DBT Masses Automatic Segmentation Using U-Net Neural Networks. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1139547
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139547