人工智能
分割
计算机科学
扫描仪
联营
模式识别(心理学)
乳腺癌
卷积神经网络
计算机视觉
乳腺肿瘤
棱锥(几何)
癌症
医学
数学
内科学
几何学
作者
Yiyao Liu,Yi Yang,Wei Jiang,Tianfu Wang,Baiying Lei
标识
DOI:10.1109/embc46164.2021.9629523
摘要
Breast cancer has become the primary factor threatening women's health. Automated breast volume scanner (ABVS) is applied for automatic scanning which is meaningful for the rapid and accurate detection of breast tumor. However, accurate segmentation of tumor regions is a huge challenge for clinicians from the ABVS images since it has the large image size and low data quality. Therefore, we propose a novel 3D deep convolutional neural network for automatic breast tumor segmentation from ABVS data. The structure based on 3D U-Net is designed with attention mechanism and transformer layers to optimize the extracted image features. In addition, we integrate the atrous spatial pyramid pooling block and the deep supervision for further performance improvement. The experimental results demonstrate that our model has achieved dice coefficient of 76.36% for 3D segmentation of breast tumor via our self-collected data.
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