Professor Julia Schnabel Academics Chair in Computational Imaging Contact details julia.schnabel@kcl.ac.uk +44 (0) 20 7848 9561 @ja_schnabel
Deep Learning for PET Image Reconstruction Reader, A., Corda, G., Mehranian, A., da Costa-Luis, C. O., Ellis, S. & Schnabel, J., 1 Aug 2020, (Accepted/In press) In : Transactions on Radiation and Plasma Medical Sciences. 27 p. Research output: Contribution to journal - Review article A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology Clough, J., Byrne, N., Oksuz, I., Zimmer, V., Schnabel, J. & King, A., 25 Jul 2020, (Accepted/In press) In : IEEE Transactions on Pattern Analysis and Machine Intelligence. Research output: Contribution to journal - Article Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation Oksuz, I., Clough, J., Ruijsink, B., Puyol Anton, E., Bustin, A., Lima da Cruz, G., Prieto Vasquez, C., King, A. & Schnabel, J., 3 Jul 2020, (Accepted/In press) In : IEEE Transactions on Medical Imaging. Research output: Contribution to journal - Article Tumour subregion analysis of colorectal liver metastases using semi-automated clustering based on DCE-MRI: Comparison with histological subregions and impact on pharmacokinetic parameter analysis Franklin, J. M., Irving, B., Papiez, B. W., Kallehauge, J. F., Wang, L. M., Goldin, R. D., Harris, A. L., Anderson, E. M., Schnabel, J. A., Chappell, M. A., Brady, M., Sharma, R. A. & Gleeson, F. V., May 2020, In : European Journal of Radiology. 126, 108934. Research output: Contribution to journal - Article. DOIs: https://doi.org/10.1016/j.ejrad.2020.108934 A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography Fuin, N., Bustin, A., Küstner, T., Oksuz, I., Clough, J., King, A. P., Schnabel, J. A., Botnar, R. M. & Prieto, C., 27 Apr 2020, In : Magnetic Resonance Imaging. Research output: Contribution to journal - Article. DOIs: https://doi.org/10.1016/j.mri.2020.04.007 Guest Editorial: Deep Learning in Ultrasound Imaging Shan, C., Tan, T., Wu, S. & Schnabel, J. A., Apr 2020, In : IEEE Journal of Biomedical and Health Informatics. 24, 4, p. 929-930 2 p., 9057768. Research output: Contribution to journal - Editorial. DOIs: https://doi.org/10.1109/JBHI.2020.2975858 An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy Sharib, A., Schnabel, J. & Grisan, E., 17 Feb 2020, In : Scientific Reports. 10, 1, 2748. Research output: Contribution to journal - Article. DOIs: https://doi.org/10.1038/s41598-020-59413-5 Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time Magnetti, C., Zimmer, V., Ghavami, N., Skelton, E., Matthew, J., Lloyd, K., Hajnal, J., Schnabel, J. A. & Gomez, A., 1 Jan 2020, Medical Image Understanding and Analysis - 24th Annual Conference, MIUA 2020, Proceedings. Papiez, B. W., Namburete, A. I. L., Yaqub, M., Noble, J. A. & Yaqub, M. (eds.). SPRINGER, p. 423-435 13 p. (Communications in Computer and Information Science; vol. 1248 CCIS). Research output: Chapter in Book/Report/Conference proceeding - Conference paper. DOIs: https://doi.org/10.1007/978-3-030-52791-4_33 Image-based artefact removal in laser scanning microscopy Papiez, B. W., Markelc, B., Brown, G., Muschel, R. J., Brady, M. & Schnabel, J. A., Jan 2020, In : IEEE Transactions on Biomedical Engineering. 67, 1, p. 79-87 9 p., 8701671. Research output: Contribution to journal - Article. DOIs: https://doi.org/10.1109/TBME.2019.2908345 Model-Based and Data-Driven Strategies in Medical Image Computing Rueckert, D. & Schnabel, J. A., Jan 2020, In : Proceedings of the IEEE. 108, 1, p. 110-124 15 p., 8867900. Research output: Contribution to journal - Article. DOIs: https://doi.org/10.1109/JPROC.2019.2943836 View all publications