ACL-DUNet: A tumor segmentation method based on multiple attention and densely connected breast ultrasound images

被引:0
作者
Hao, Zhang [1 ]
He, Liang [2 ,3 ]
Guo, Wenjia [4 ]
Ma, Jing [1 ]
Sun, Gang [5 ,6 ]
Ma, Hongbing [2 ,3 ]
机构
[1] Xinjiang Univ, Sch Comp Sci & Technol, Urumqi, Xinjiang, Peoples R China
[2] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China
[3] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing, Peoples R China
[4] Xinjiang Med Univ, Canc Inst, Affiliated Canc Hosp, Urumqi, Xinjiang, Peoples R China
[5] Xinjiang Med Univ, Dept Breast & Thyroid Surg, Affiliated Canc Hosp, Urumqi, Xinjiang, Peoples R China
[6] Xinjiang Canc Ctr, Key Lab Oncol Xinjiang Uyghur Autonomous Reg, Urumqi, Xinjiang, Peoples R China
来源
PLOS ONE | 2024年 / 19卷 / 11期
关键词
CONVOLUTIONAL NEURAL-NETWORKS; LESION SEGMENTATION; CLASSIFICATION; MASSES; NET;
D O I
10.1371/journal.pone.0307916
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for diagnosing breast cancer, and ultrasound is widely used for screening as a non-invasive and effective method for breast examination. In this study, we used the Mendeley and BUSI datasets, comprising 250 images (100 benign, 150 malignant) and 780 images (133 normal, 487 benign, 210 malignant), respectively. The datasets were split into 80% for training and 20% for validation. The accurate measurement and characterization of different breast tumors play a crucial role in guiding clinical decision-making. The area and shape of the different breast tumors detected are critical for clinicians to make accurate diagnostic decisions. In this study, a deep learning method for mass segmentation in breast ultrasound images is proposed, which uses densely connected U-net with attention gates (AGs) as well as channel attention modules and scale attention modules for accurate breast tumor segmentation.The densely connected network is employed in the encoding stage to enhance the network's feature extraction capabilities. Three attention modules are integrated in the decoding stage to better capture the most relevant features. After validation on the Mendeley and BUSI datasets, the experimental results demonstrate that our method achieves a Dice Similarity Coefficient (DSC) of 0.8764 and 0.8313, respectively, outperforming other deep learning approaches. The source code is located at github.com/zhanghaoCV/plos-one.
引用
收藏
页数:18
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