Information for Collaborators
Founded in 1995 as technology startup Voxar, Edinburgh-based Toshiba Medical Visualization Systems has been a Research & Development subsidiary of Toshiba Medical Systems Corporation since 2009. Our advanced visualization and healthcare imaging informatics software is integrated into tens of thousands of CT, MRI, Ultrasound and Interventional X-Ray scanners and diagnostic workstations deployed in hospitals throughout the world.
Our staff collaborate closely with colleagues at Toshiba Medical’s headquarters in Japan and at sibling R&D centres globally: Toshiba Medical Research Institute USA, Vital Images, Toshiba Medical Systems (China) and Olea Medical
- Jimenez-del-Toro, O. et al. (2016) ‘Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks’, IEEE Transactions on Medical Imaging, vol. 35, no. 11, pp. 2459-2475.
- Rosmini, S., Treibel, TA. et al. (2016) ‘Cardiac computed tomography for the detection of cardiac amyloidosis’, Journal of Cardiovascular Computed Tomography, in press.
- Nakatsugawa, M. et al. (2016) ‘Radiomic Analysis of Salivary Glands and Its Role for Predicting Xerostomia in Irradiated Head and Neck Cancer Patients’, International Journal of Radiation Oncology • Biology • Physics, vol. 96, no. 2, p. S217.
- Blobel, J. et al. (2016) ‘Calibration of coronary calcium scores determined using iterative image reconstruction (AIDR 3D) at 120, 100, and 80 kVp’, Medical Physics, vol. 43, no. 4, pp. 1921-1932.
- O’Neil, A., Dabbah, M. and Poole, I. (2016) ‘Cross-Modality Anatomical Landmark Detection Using Histograms of Unsigned Gradient Orientations and Atlas Location Autocontext’, in Wang, L. et al (eds) Machine Learning in Medical Imaging, Springer International Publishing, pp. 139-146.
- O’Neil, A. et al. (2016) ‘Automatic identification and tracking of the major arteries in Magnetic Resonance Angiography scans’, Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 29, no. 1.
- Tang, Q. et al. (2015) ‘Motion estimation and compensation for coronary artery and myocardium in cardiac CT’, Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94120Q.
- Fuchs, A. et al. (2015) ‘Feasibility of coronary calcium and stent image subtraction using 320-detector row CT angiography’, Journal of Cardiovascular Computed Tomography, vol. 9, no. 5, pp. 393-398.
- Tamerus, A., Washbrook, A. and Wyeth, D. (2015) ‘Acceleration of ensemble machine learning methods using many-core devices’, Journal of Physics: Conference Series, vol. 664, no. 9, pp. 092026.
- Lisowska, A. et al. (2015) ‘An Evaluation of Supervised, Novelty-Based and Hybrid Approaches to Fall Detection Using Silmee Accelerometer Data’, The IEEE International Conference on Computer Vision (ICCV) Workshops, 2015, pp. 10-16.
- O’Neil, A., Murphy, S. and Poole, I. (2015) ‘Anatomical landmark detection in CT data by learned atlas location autocontext’, Proceedings of the 19th Conference on Medical Image Understanding and Analysis, pp. 189-194.
- Gondim Teixeira, P. et al. (2014) ‘Bone Marrow Edema Pattern Identification in Patients With Lytic Bone Lesions Using Digital Subtraction AngiographyLike Bone Subtraction on Large-Area Detector Computed Tomography’, Investigative Radiology, vol. 49, no. 3, pp. 156-164
- Tang, Q. et al. (2014) ‘A combined local and global motion estimation and compensation method for cardiac CT’, Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 903304.
- O’Neil, A. et al. (2014) ‘Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans’, Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90342S.
- Murphy, S. et al. (2014) ‘Fast, Simple, Accurate Multi-atlas Segmentation of the Brain’, in Ourselin, S. and Modat, M. (eds) Biomedical Image Registration Springer International Publishing, pp.1-10.
- Dabbah, M. et al. (2014) ‘Detection and location of 127 anatomical landmarks in diverse CT datasets’, Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903415.
- Kirişli, H.A. et al. (2013) ‘Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography’, Medical Image Analysis, vol. 17, no. 8, pp. 859-876
- Mohr, B., Masood, S. and Plakas, C. (2012) ‘Accurate lumen segmentation and stenosis detection and quantification in coronary CTA’, in Proceedings of 3D Cardiovascular Imaging: a MICCAI segmentation challenge workshop.
- Piper, J. et al. (2012) ‘Objective evaluation of the correction by non-rigid registration of abdominal organ motion in low-dose 4D dynamic contrastenhanced CT’, Physics in Medicine and Biology, vol. 57, no. 6, p. 1701.
- Dickie, D.A., Job, D.E., Poole, I. et al. (2012) ‘Do brain image databanks support understanding of normal ageing brain structure? A systematic review’, European Radiology, vol. 22, no. 7, pp. 1385-1394.
- Murphy, S., Akinyemi, A., Steel, J. et al. (2012) ‘Multi-compartment heart segmentation in CT angiography using a spatially varying gaussian classifier’, International Journal of Computer Assisted Radiology and Surgery, vol. 7, no. 6, pp. 829-836.
- Akinyemi, A. et al. (2012) ‘Optimal atlas selection using image similarities in a trained regression model to predict performance’, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1264-1267.
- Akinyemi, A. et al. (2009) ‘Automatic labelling of coronary arteries’, 2009 17th European Signal Processing Conference, pp. 1562-1566.
Our growing IP portfolio includes 50 granted patents – and others pending – protecting aspects of medical image registration, segmentation, visualization and user interface design.