IEEE Transactions on Medical Imaging

Journal Information
ISSN / EISSN : 0278-0062 / 1558-254X
Current Publisher: IEEE (10.1109)
Former Publisher: , Institute of Electrical and Electronics Engineers (IEEE) (10.1109) , Institute of Electrical and Electronics Engineers (IEEE) (10.1109) , Institute of Electrical and Electronics Engineers (IEEE) (10.1109) IEEE (10.1109)
Total articles ≅ 6,464
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Latest articles in this journal

Yixing Huang, Alexander Preuhs, Michael Manhart, Guenter Lauritsch, Andreas Maier
IEEE Transactions on Medical Imaging, pp 1-1; doi:10.1109/tmi.2021.3072568

The publisher has not yet granted permission to display this abstract.
Fatemeh Taheri Dezaki, Christina Luong, Tom Ginsberg, Robert Rohling, Ken Gin, Purang Abolmaesumi, Teresa Tsang
IEEE Transactions on Medical Imaging, pp 1-1; doi:10.1109/tmi.2021.3071951

Abstract:
In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements. However, in emergencies or point-of-care situations, acquiring an ECG is often not an option, hence motivating the need for alternative temporal synchronization methods. Here, we propose Echo-SyncNet, a self-supervised learning framework to synchronize various cross-sectional 2D echo series without any human supervision or external inputs. The proposed framework takes advantage of two types of supervisory signals derived from the input data: spatiotemporal patterns found between the frames of a single cine (intra-view self-supervision) and interdependencies between multiple cines (inter-view self-supervision). The combined supervisory signals are used to learn a feature-rich and low dimensional embedding space where multiple echo cines can be temporally synchronized. Two intra-view self-supervisions are used, the first is based on the information encoded by the temporal ordering of a cine (temporal intra-view) and the second on the spatial similarities between nearby frames (spatial intra-view). The inter-view self-supervision is used to promote the learning of similar embeddings for frames captured from the same cardiac phase in different echo views. We evaluate the framework with multiple experiments: 1) Using data from 998 patients, Echo-SyncNet shows promising results for synchronizing Apical 2 chamber and Apical 4 chamber cardiac views, which are acquired spatially perpendicular to each other; 2) Using data from 3070 patients, our experiments reveal that the learned representations of Echo-SyncNet outperform a supervised deep learning method that is optimized for automatic detection of fine-grained cardiac cycle phase; 3) We go one step further and show the usefulness of the learned representations in a one-shot learning scenario of cardiac key-frame detection. Without any fine-tuning, key frames in 1188 validation patient studies are identified by synchronizing them with only one labeled reference cine. We do not make any prior assumption about what specific cardiac views are used for training, and hence we show that Echo-SyncNet can accurately generalize to views not present in its training set. Project repository: github.com/fatemehtd/Echo-SyncNet.
Gongfa Jiang, Jun Wei, Yuesheng Xu, Zilong He, Hui Zeng, Jiefang Wu, Genggeng Qin, Weiguo Chen, Yao Lu
IEEE Transactions on Medical Imaging, pp 1-1; doi:10.1109/tmi.2021.3071544

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Peng Zhang, Guangda Fan, Tongtong Xing, Fan Song, Guanglei Zhang
IEEE Transactions on Medical Imaging, pp 1-1; doi:10.1109/tmi.2021.3071556

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Saransh Sharma, Aditya Telikicherla, Grace Ding, Fatemeh Aghlmand, Arian Hashemi Talkhooncheh, Mikhail G. Shapiro, Azita Emami
IEEE Transactions on Medical Imaging, pp 1-1; doi:10.1109/tmi.2021.3071120

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Loic Peter, Daniel C. Alexander, Caroline Magnain, Juan Eugenio Iglesias
IEEE Transactions on Medical Imaging, pp 1-1; doi:10.1109/tmi.2021.3070842

Abstract:
Landmark correspondences are a widely used type of gold standard in image registration. However, the manual placement of corresponding points is subject to high inter-user variability in the chosen annotated locations and in the interpretation of visual ambiguities. In this paper, we introduce a principled strategy for the construction of a gold standard in deformable registration. Our framework: (i) iteratively suggests the most informative location to annotate next, taking into account its redundancy with previous annotations; (ii) extends traditional pointwise annotations by accounting for the spatial uncertainty of each annotation, which can either be directly specified by the user, or aggregated from pointwise annotations from multiple experts; and (iii) naturally provides a new strategy for the evaluation of deformable registration algorithms. Our approach is validated on four different registration tasks. The experimental results show the efficacy of suggesting annotations according to their informativeness, and an improved capacity to assess the quality of the outputs of registration algorithms. In addition, our approach yields, from sparse annotations only, a dense visualization of the errors made by a registration method. The source code of our approach supporting both 2D and 3D data is publicly available at https://github.com/LoicPeter/evaluation-deformable-registration.
Jie Lian, Jingyu Liu, Shu Zhang, Kai Gao, Xiaoqing Liua, Dingwen Zhangb, Yizhou Yu
IEEE Transactions on Medical Imaging, pp 1-1; doi:10.1109/tmi.2021.3070847

The publisher has not yet granted permission to display this abstract.
Cagla Ozsoy, Andrea Cossettini, Ali Ozbek, Sergei Vostrikov, Pascal Hager, Xose Luis Dean-Ben, Luca Benini, Daniel Razansky
IEEE Transactions on Medical Imaging, pp 1-1; doi:10.1109/tmi.2021.3070833

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Jie Zhang, Jianfeng Wu, Qingyang Li, Richard J. Caselli, Paul M. Thompson, Jieping Ye, Yalin Wang
IEEE Transactions on Medical Imaging, pp 1-1; doi:10.1109/tmi.2021.3070780

The publisher has not yet granted permission to display this abstract.
A. Avdo Celik, Chang-Hoon Choi, Lutz Tellmann, Claire Rick, N. Jon Shah, Jorg Felder
IEEE Transactions on Medical Imaging, pp 1-1; doi:10.1109/tmi.2021.3070626

The publisher has not yet granted permission to display this abstract.
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