Segmentation of Breast Masses in Digital Mammography Based on U-Net Deep Convolutional Neural Networks

被引：0
作者：
A. N. Ruchay
V. I. Kober
K. A. Dorofeev
V. N. Karnaukhov
M. G. Mozerov
机构：
[1] Chelyabinsk State University,
[2] Kharkevich Institute for Information Transmission Problems,undefined
[3] Russian Academy of Sciences,undefined
[4] South Ural State University (National Research University),undefined
[5] Center of Scientific Research and Higher Education,undefined
来源：
Journal of Communications Technology and Electronics | 2022年 / 67卷
关键词：
segmentation; digital mammography; U-Net deep convolutional neural network; data augmentation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we propose a new algorithm for segmentation of breast masses in digital mammography based on U-Net deep convolutional neural networks. All raw mammogram images are preprocessed to improve segmentation reliability. Deep learning was carried out using additional methods of data augmentation and fine tuning of the neural network. The performance of the proposed algorithm for segmentation of breast masses using the known CBIS-DDSM database is discussed.
引用
收藏
页码：1531 / 1541
页数：10
相关论文
共 50 条
[31]   Automatic Tumor Segmentation by Means of Deep Convolutional U-Net With Pre-Trained Encoder in PET Images [J].
Lu, Yongzhou ;
Lin, Jinqiu ;
Chen, Sheng ;
He, Hui ;
Cai, Yuantao .
IEEE ACCESS, 2020, 8 :113636-113648
[32]   A Multi-Objective Semantic Segmentation Algorithm Based on Improved U-Net Networks [J].
Hao, Xuejie ;
Yin, Lizeyan ;
Li, Xiuhong ;
Zhang, Le ;
Yang, Rongjin .
REMOTE SENSING, 2023, 15 (07)
[33]   Acral melanocytic lesion segmentation with a convolution neural network (U-Net) [J].
Jaworek-Korjakowska, Joanna .
MEDICAL IMAGING 2019: COMPUTER-AIDED DIAGNOSIS, 2019, 10950
[34]   DRA U-Net: An Attention based U-Net Framework for 2D Medical Image Segmentation [J].
Zhang, Xian ;
Feng, Ziyuan ;
Zhong, Tianchi ;
Shen, Sicheng ;
Zhang, Ruolin ;
Zhou, Lijie ;
Zhang, Bo ;
Wang, Wendong .
2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, :3936-3942
[35]   Deep Learning with Limited Data: Organ Segmentation Performance by U-Net [J].
Bardis, Michelle ;
Houshyar, Roozbeh ;
Chantaduly, Chanon ;
Ushinsky, Alexander ;
Glavis-Bloom, Justin ;
Shaver, Madeleine ;
Chow, Daniel ;
Uchio, Edward ;
Chang, Peter .
ELECTRONICS, 2020, 9 (08) :1-12
[36]   Automated segmentation of vertebral cortex with 3D U-Net-based deep convolutional neural network [J].
Li, Yang ;
Yao, Qianqian ;
Yu, Haitao ;
Xie, Xiaofeng ;
Shi, Zeren ;
Li, Shanshan ;
Qiu, Hui ;
Li, Changqin ;
Qin, Jian .
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2022, 10
[37]   ARDC-UNet retinal vessel segmentation with adaptive residual deformable convolutional based U-Net [J].
Naik, N. V. ;
Hyma, J. ;
Reddy, P. V. G. D. Prasad .
MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (33) :78747-78768
[38]   Digital infrared thermal imaging system based breast cancer diagnosis using 4D U-Net segmentation [J].
Gomathi, P. ;
Muniraj, C. ;
Periasamy, P. S. .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 85
[39]   Deep convolutional neural networks for mammography: advances, challenges and applications [J].
Abdelhafiz, Dina ;
Yang, Clifford ;
Ammar, Reda ;
Nabavi, Sheida .
BMC BIOINFORMATICS, 2019, 20 (Suppl 11)
[40]   Histopathological Image Segmentation Using U-Net Based Models [J].
Hatipoglu, Nuh ;
Bilgin, Gokhan .
TIP TEKNOLOJILERI KONGRESI (TIPTEKNO'21), 2021,
12345