Self-co-attention neural network for anatomy segmentation in whole breast ultrasound

Lei Baiying; Huang Shan; Li Hang; Li Ran; Bian Cheng; Chou Yi-Hong; Qin Jing; Zhou Peng; Gong Xuehao; Cheng Jie-Zhi

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Self-co-attention neural network for anatomy segmentation in whole breast ultrasound

机译：整个乳房超声中解剖分割的自共关神经网络

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摘要

The automated whole breast ultrasound (AWBUS) is a new breast imaging technique that can depict the whole breast anatomy. To facilitate the reading of AWBUS images and support the breast density estimation, an automatic breast anatomy segmentation method for AWBUS images is proposed in this study. The problem at hand is quite challenging as it needs to address issues of low image quality, ill-defined boundary, large anatomical variation, etc. To address these issues, a new deep learning encoder-decoder segmentation method based on a self-co-attention mechanism is developed. The self-attention mechanism is comprised of spatial and channel attention module (SC) and embedded in the ResNeXt (i.e., Res-SC) block in the encoder path. A non-local context block (NCB) is further incorporated to augment the learning of high-level contextual cues. The decoder path of the proposed method is equipped with the weighted up-sampling block (WUB) to attain class-specific better up-sampling effect. Meanwhile, the co-attention mechanism is also developed to improve the segmentation coherence among two consecutive slices. Extensive experiments are conducted with comparison to several the state-of-the-art deep learning segmentation methods. The experimental results corroborate the effectiveness of the proposed method on the difficult breast anatomy segmentation problem on AWBUS images. (C) 2020 Elsevier B.V. All rights reserved.

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来源
《Medical image analysis》 |2020年第1期|共14页
作者
Lei Baiying; Huang Shan; Li Hang; Li Ran; Bian Cheng; Chou Yi-Hong; Qin Jing; Zhou Peng; Gong Xuehao; Cheng Jie-Zhi;
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作者单位

Shenzhen Univ Natl Reg Key Technol Engn Lab Med Ultrasound Guangdong Key Lab Biomed Measurements;

Shenzhen Univ Natl Reg Key Technol Engn Lab Med Ultrasound Guangdong Key Lab Biomed Measurements;

Shenzhen Univ Natl Reg Key Technol Engn Lab Med Ultrasound Guangdong Key Lab Biomed Measurements;

Shenzhen Univ Natl Reg Key Technol Engn Lab Med Ultrasound Guangdong Key Lab Biomed Measurements;

Shenzhen Univ Natl Reg Key Technol Engn Lab Med Ultrasound Guangdong Key Lab Biomed Measurements;

Yuanpei Univ Med Technol Dept Med Imaging &

Radiol Technol Hsinchu Taiwan;

Hong Kong Polytech Univ Sch Nursing Ctr Smart Hlth Hong Kong Peoples R China;

Shenzhen Univ Peoples Hosp Shenzhen 2 Affiliated Hosp 1 Dept Ultrasound Shenzhen Peoples Hosp 2;

Shenzhen Univ Peoples Hosp Shenzhen 2 Affiliated Hosp 1 Dept Ultrasound Shenzhen Peoples Hosp 2;

Shanghai United Imaging Intelligence Co Ltd UII Shanghai Peoples R China;

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原文格式 PDF
正文语种 eng
中图分类影像诊断学;
关键词
Breast anatomy segmentation; Self-co-attention mechanism; Non-local cue; Encoder-decoder architecture;

机译：乳房解剖分割;自我关注机制;非本地提示;编码器 - 解码器架构;

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Self-co-attention neural network for anatomy segmentation in whole breast ultrasound

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