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RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D Ultrasound Imaging Shape Reconstruction | |
2024-08-14 | |
会议录名称 | ARXIV (IF:0.402[JCR-2005],0.000[5-Year]) |
ISSN | 0302-9743 |
卷号 | 15004 |
发表状态 | 已发表 |
DOI | arXiv:2408.07325 |
摘要 | The reconstruction of high-quality shape geometry is crucial for developing freehand 3D ultrasound imaging. However, the shape reconstruction of multi-view ultrasound data remains challenging due to the elevation distortion caused by thick transducer probes. In this paper, we present a novel learning-based framework RoCoSDF, which can effectively generate an implicit surface through continuous shape representations derived from row-column scanned datasets. In RoCoSDF, we encode the datasets from different views into the corresponding neural signed distance function (SDF) and then operate all SDFs in a normalized 3D space to restore the actual surface contour. Without requiring pre-training on large-scale ground truth shapes, our approach can synthesize a smooth and continuous signed distance field from multi-view SDFs to implicitly represent the actual geometry. Furthermore, two regularizers are introduced to facilitate shape refinement by constraining the SDF near the surface. The experiments on twelve shapes data acquired by two ultrasound transducer probes validate that RoCoSDF can effectively reconstruct accurate geometric shapes from multi-view ultrasound data, which outperforms current reconstruction methods. |
关键词 | Multi-view reconstruction Neural shape representation Freehand 3D ultrasound |
会议名称 | 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) |
出版地 | GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND |
会议地点 | Palmeraie Conf Ctr,Marrakesh,MOROCCO |
会议日期 | OCT 06-10, 2024 |
URL | 查看原文 |
收录类别 | CPCI-S |
语种 | 英语 |
资助项目 | Natural Science Foundation of China[12074258] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Software Engineering ; Engineering, Electrical& Electronic |
WOS记录号 | PPRN:91378628 |
出版者 | SPRINGER INTERNATIONAL PUBLISHING AG |
EISSN | 1611-3349 |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/414213 |
专题 | 信息科学与技术学院 信息科学与技术学院_PI研究组_郑锐组 信息科学与技术学院_硕士生 信息科学与技术学院_博士生 信息科学与技术学院_PI研究组_马月昕 |
通讯作者 | Zheng, Rui |
作者单位 | 1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China 2.ShanghaiTech Univ, Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai, Peoples R China |
第一作者单位 | 信息科学与技术学院 |
通讯作者单位 | 信息科学与技术学院; 上海科技大学 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Chen, Hongbo,Gao, Yuchong,Zhang, Shuhang,et al. RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D Ultrasound Imaging Shape Reconstruction[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2024. |
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