检索结果-内蒙古大学图书馆

25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Zhang, Jiadong Cui, Zhiming Jiang, Caiwen Zhang, Jingyang Gao, Fei Shen, Dinggang ShanghaiTech Univ Sch Biomed Engn Shanghai 201210 Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai 200230 Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Positron emission tomography (PET) is an important medical imaging technique, especially for brain and cancer disease diagnosis. Modern PET scanner is usually combined with computed tomography (CT), where CT image is used for anatomical localization, PET attenuation correction, and radiotherapy treatment planning Considering radiation dose of CT image as well as increasing spatial resolution of PET image, there is a growing demand to synthesize CT image from PET image (without scanning CT) to reduce risk of radiation exposure. However, most existing works perform learning-based image synthesis to construct cross-modality mapping only in the image domain, without considering of the projection domain, leading to potential physical inconsistency. To address this problem, we propose a novel PET-CT synthesis framework by exploiting dual-domain information (i.e., image domain and projection domain). Specifically, we design both image domain network and projection domain network to jointly learn high-dimensional mapping from PET to CT. The image domain and the projection domain can be connected together with a forward projection (FP) and a filtered back projection (FBP). To further help the PET-to-CT synthesis task, we also design a secondary CT-toPET synthesis task with the same network structure, and combine the two tasks into a bidirectional mapping framework with several closed cycles. More importantly, these cycles can serve as cycle-consistent losses to further help network training for better synthesis performance. Extensive validations on the clinical PET-CT data demonstrate the proposed PET-CT synthesis framework outperforms the state-of-the-art (SOTA) medical image synthesis methods with significant improvements.

关键词： Medical image synthesis Dual-domain information cycle-consistent constraint

来源：评论

学校读者我要写书评

暂无评论

Self-supervised monocular depth estimation via multiple bilateral consistency

引用

MULTIMEDIA SYSTEMS 2025年第2期31卷 1-11页

作者： Lu, Zhengyang Chen, Ying Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214026 Peoples R China

Recent researches on deep-learning methods have shown enormous promise for depth estimates. Although self-supervised estimators alleviate laborious annotation, numerous pixels fail to match the corresponding ones in adjacent frames. Single re-projection loss functions are oversimplified for stereoscopic adjacent frame constraints. In this work, we constrain the primitive inter-frame-supervised depth estimation via multiple bilateral consistency, which builds a bi-directional mapping between adjacent frames with inherent properties, allowing to develop pose-consistent and depth-consistent models. For the mismatching pixels in adjacent frames, a cycle-consistency framework reconstructs depth maps as scene images with an additional symmetrical re-rendering network. Pose-consistent constraint aims to ensure the reversibility of ego-motion transformations between adjacent frames, and depth-consistent constraint strives to ensure the continuity of adjacent frames' depths. With the joint optimization of three independent modules, depth network, pose network and re-rendering network, the proposed framework yields state-of-the-art metrics on the KITTI depth dataset pre-trained on CityScapes, with the AbsRel, SqRel, RMSE and RMS(log) decreased by 6.6%, 12.4%, 0.4%, and 2.2%, respectively.

关键词： Self-supervised depth estimation cycle-consistent constraint Bilateral constraint Convolution neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：