Добірка наукової літератури з теми "Inter-modal Registration"
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Статті в журналах з теми "Inter-modal Registration"
Wang, Chengjia, Guang Yang, and Giorgos Papanastasiou. "Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis." Sensors 22, no. 6 (March 9, 2022): 2125. http://dx.doi.org/10.3390/s22062125.
Повний текст джерелаCopson, Bridget, Sudanthi Wijewickrema, Christopher Slinger, Daniel Youssef, Jean-Marc Gerard, and Stephen O’Leary. "Definition of a coordinate system for multi-modal images of the temporal bone and inner ear." PLOS ONE 19, no. 10 (October 7, 2024): e0294828. http://dx.doi.org/10.1371/journal.pone.0294828.
Повний текст джерелаWest, Malcolm, Andrew Bates, Chloe Grimmett, Cait Allen, Richard Green, Lesley Hawkins, Helen Moyses, et al. "The Wessex Fit-4-Cancer Surgery Trial (WesFit): a protocol for a factorial-design, pragmatic randomised-controlled trial investigating the effects of a multi-modal prehabilitation programme in patients undergoing elective major intra–cavity cancer surgery." F1000Research 10 (August 2, 2022): 952. http://dx.doi.org/10.12688/f1000research.55324.2.
Повний текст джерелаWest, Malcolm, Andrew Bates, Chloe Grimmett, Cait Allen, Richard Green, Lesley Hawkins, Helen Moyses, et al. "The Wessex Fit-4-Cancer Surgery Trial (WesFit): a protocol for a factorial-design, pragmatic randomised-controlled trial investigating the effects of a multi-modal prehabilitation programme in patients undergoing elective major intra–cavity cancer surgery." F1000Research 10 (September 21, 2021): 952. http://dx.doi.org/10.12688/f1000research.55324.1.
Повний текст джерелаMasoumi, Nima, Hassan Rivaz, M. Omair Ahmad, and Yiming Xiao. "DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR." International Journal of Computer Assisted Radiology and Surgery, September 29, 2022. http://dx.doi.org/10.1007/s11548-022-02749-2.
Повний текст джерелаIommi, David, Alejandra Valladares, Michael Figl, Marko Grahovac, Gabor Fichtinger, and Johann Hummel. "3D ultrasound guided navigation system with hybrid image fusion." Scientific Reports 11, no. 1 (April 23, 2021). http://dx.doi.org/10.1038/s41598-021-86848-1.
Повний текст джерелаHu, Xin, Yan Wu, Xingyu Liu, Zhikang Li, Zhifei Yang, and Ming Li. "Intra- and Inter-Modal Graph Attention Network and Contrastive Learning for SAR and Optical Image Registration." IEEE Transactions on Geoscience and Remote Sensing, 2023, 1. http://dx.doi.org/10.1109/tgrs.2023.3328368.
Повний текст джерелаAlley, Stephanie, Edward Jackson, Damien Olivié, Uulke A. van der Heide, Cynthia Ménard, and Samuel Kadoury. "Effect of magnetic resonance imaging pre-processing on the performance of model-based prostate tumor probability mapping." Physics in Medicine & Biology, October 12, 2022. http://dx.doi.org/10.1088/1361-6560/ac99b4.
Повний текст джерелаДисертації з теми "Inter-modal Registration"
Ndzimbong, William Brice. "Recalage automatique des images echographiques tridimensionnelles et tomodensitométriques du rein." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD047.
Повний текст джерелаAutomatic registration between abdominal ultrasound (US) and computed tomography (CT) images is needed to enhance interventional guidance in kidney surgery. However, it remains an open research challenge. One striking limitation is the lack of public datasets that comprise images of the same patient in both modalities (paired datasets). This has hindered methodological progress, as well as prevented a systematic comparison of state-of-the-art methods. Another limitation is the lack of robust methods capable of solving registration without manual initialization (’global’ methods). This thesis aims to overcome these challenges with several research contributions. The first contribution is a novel dataset with paired transabdominal 3D US and CT kidney images from 48 human patients that includes segmentation and anatomical landmark annotations from two expert radiographers. In addition to the dataset, annotation consistency is analyzed, and its value assessed by benchmarking methods that tackle two fundamental tasks : automatic kidney segmentation and inter-modal image registration. The findings show that both challenges are still open, and the dataset should serve as an important resource for advancing both topics. As a second main contribution, an automatic method for global registration of kidneys in 3D US and CT images is proposed. This method handles registration ambiguity caused by the organ’s natural symmetry. Combined with a registration refinement algorithm, it achieves robust and accurate kidney registration while avoiding manual initialization. The method has several other important applications, including inter-modal image translation and image synthesis, as well as label transfer between modalities
Частини книг з теми "Inter-modal Registration"
Salari, Soorena, Amirhossein Rasoulian, Hassan Rivaz, and Yiming Xiao. "FocalErrorNet: Uncertainty-Aware Focal Modulation Network for Inter-modal Registration Error Estimation in Ultrasound-Guided Neurosurgery." In Lecture Notes in Computer Science, 689–98. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43996-4_66.
Повний текст джерелаТези доповідей конференцій з теми "Inter-modal Registration"
Li, Xia, Thomas E. Yankeelov, Glenn Rosen, John C. Gore, and Benoit M. Dawant. "Multi-modal inter-subject registration of mouse brain images." In Medical Imaging, edited by Joseph M. Reinhardt and Josien P. W. Pluim. SPIE, 2006. http://dx.doi.org/10.1117/12.652407.
Повний текст джерелаChappelow, Jonathan, Anant Madabhushi, Mark Rosen, John Tomaszeweski, and Michael Feldman. "A COMBINED FEATURE ENSEMBLE BASED MUTUAL INFORMATION SCHEME FOR ROBUST INTER-MODAL, INTER-PROTOCOL IMAGE REGISTRATION." In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2007. http://dx.doi.org/10.1109/isbi.2007.356934.
Повний текст джерелаBashkanov, Oleksii, Anneke Meyer, Daniel Schindele, Martin Schostak, Klaus-Dietz Tonnies, Christian Hansen, and Marko Rak. "Learning Multi-Modal Volumetric Prostate Registration With Weak Inter-Subject Spatial Correspondence." In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433848.
Повний текст джерелаPapp, L., N. Zsoter, G. Szabo, C. Bejan, E. Szimjanovszki, and M. Zuhayra. "Parallel registration of multi-modal medical image triples having unknown inter-image geometry." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5335168.
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