Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
CEUSegNet: A Cross-Modality Lesion Segmentation Network for Contrast-Enhanced Ultrasound | |
Zheling MENG1,2![]() ![]() | |
2022-04 | |
会议名称 | IEEE International Symposium on Biomedical Imaging (ISBI) |
会议日期 | 2022.3.28-31 |
会议地点 | Kolkata, India |
会议录编者/会议主办者 | IEEE |
摘要 | Contrast-enhanced ultrasound (CEUS) is an effective imaging tool to analyze spatial-temporal characteristics of lesions and diagnose or predict diseases. However, delineating lesions frame by frame is a time-consuming work, which brings challenges to analyzing CEUS videos with deep learning technology. In this paper, we proposed a novel U-net-like network with dual top-down branches and residual connections, named CEUSegNet. CEUSegNet takes US and CEUS part of a dual-amplitude CEUS image as inputs. Cross-modality Segmentation Attention (CSA) and Cross-modality Feature Fusion (CFF) are designed to fuse US and CEUS features on multiple scales. Through our method, lesion position can be determined exactly under the guidance of US and then the region of interest can be delineated in CEUS image. Results show CEUSegNet can achieve a comparable performance with clinicians on metastasis cervical lymph nodes and breast lesion dataset. |
收录类别 | EI |
七大方向——子方向分类 | 人工智能+医疗 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51648 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Zheling MENG; Kun WANG |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学人工智能学院 3.兰州大学第二医院 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zheling MENG,Yangyang ZHU,Xiao FAN,et al. CEUSegNet: A Cross-Modality Lesion Segmentation Network for Contrast-Enhanced Ultrasound[C]//IEEE,2022. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
CEUSegNet20220117.pd(1363KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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