Our Publications

Clinical Research Image

Publication Research

While gradually building up a strong tradition in publishing, our research teams have developed an internal publication strategy where they work either individually or collaboratively on publications linked to their current projects.

To assist with their research they have established strong links with local universities, including Glasgow, Edinburgh, Heriot Watt, Aberdeen and St Andrews, and work with both clinical and academic collaborators to maintain a long term strategy to deliver new and exciting projects. To further support their research our scientists have opportunities to attend international conferences focused on medical imaging, deep learning and artificial intelligence.


Daykin, M., Sellathurai, M. and Poole, I., 2018, July. A Comparison of Unsupervised Abnormality Detection Methods for Interstitial Lung Disease . In Annual Conference on Medical Image Understanding and Analysis (pp. 287-298). Springer, Cham.

Lisowska, A., O'Neil, A. and Poole, I. (2018) Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data . In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, January 2018, ISBN 978-989-758-281-3, pages 77-82.

Mikhael, S. and Gray, C. (2018) Masks2Metrics (M2M): A Matlab Toolbox for Gold Standard Morphometrics . Journal of Open Source Software, 3(22), 436.

Mikhael, S., Mair, G., Valdés Hernández, M.d.C., Hoogendoorn, C., Wardlaw, J., Bastin, M., and Pernet, C., Manually-parcellated gyral data accounting for all known anatomical variability. Scientific Data, in press.

O'Neil, A.Q., Kascenas, A., Henry, J., Wyeth, D., Shepherd, M., Beveridge, E., Clunie, L., Sansom, C., Šeduikytė, E., Muir, K. and Poole, I., (2018). Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT data . ECCV 2018 Workshop "Geometry Meets Deep Learning".

Sloan, J., Goatman, K. and Siebert, J. (2018). Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration . In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, ISBN 978-989-758-278-3, pages 89-99.


Daykin, M., Beveridge, E., Dilys, V., Lisowska, A., Muir, K., Sellathurai, M. and Poole, I., (2017). Evaluation of an Automatic ASPECT Scoring System for Acute Stroke in Non-Contrast CT . In Annual Conference on Medical Image Understanding and Analysis (pp. 537-547). Springer, Cham.

Lisowska, A., O’Neil, A., Dilys, V., Beveridge, E., Muir, K., Poole, I. (2017). Dense vessel detection using an anatomically-aware CNN in non-contrast CT scans . ‘Medical Imaging meets NIPS’ satellite workshop at NIPS 2017.

Lisowska, A., Beveridge, E., O’Neil, A., Dilys, V., Muir, K. and Poole, I. (2017). Evaluation of Dense Vessel Detection in NCCT Scans . In International Joint Conference on Biomedical Engineering Systems and Technologies (pp. 134-145). Springer, Cham.

Lisowska, A., Beveridge, E., Muir, K., & Poole, I. (2017). Thrombus Detection in CT Brain Scans using a Convolutional Neural Network . Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies, 24–33.

Lisowska, A., O’Neil, A., Dilys, V., Daykin, M., Beveridge, E., Muir, K., McLaughlin, S., Poole, I. (2017). Context-aware convolutional neural networks for stroke sign detection in non-contrast CT scans . In Communications in Computer and Information Science, MIUA (Vol. 723, pp. 494–505).

McNeil, A., Degano, G., Poole, I., Houston, G., & Trucco, E. (2017). Comparison of automatic vessel segmentation techniques for whole body magnetic resonance angiography with limited ground truth data . In Communications in Computer and Information Science: MIUA (Vol. 723, pp. 144–155).

Mikhael, S., Hoogendoorn, C., Valdes-Hernandez, M., Pernet, C (2017). A Critical Analysis of Neuroanatomical Software Protocols Reveals Clinically Relevant Differences in Parcellation Schemes . NeuroImage, 170, 348-364.

O’Neil, A., Shepherd, M., Beveridge, E. and Goatman, K., (2017). A Comparison of Texture Features Versus Deep Learning for Image Classification in Interstitial Lung Disease . In Annual Conference on Medical Image Understanding and Analysis (pp. 743-753). Springer, Cham.

O’Neil, A. Q., Murchison, J. T., Van Beek, E. J. R., & Goatman, K. A. (2017). Crowdsourcing labels for pathological patterns in CT lung scans: Can non-experts contribute expert-quality ground truth? In: Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (MICCAI). LNCS, vol. 10552, pp. 96-105). Springer, Cham.

Tobon-Gomez, C., Stroud, T., Cameron, J., Elcock, D., Murray, A., Wyeth, D., Conway, C., Reynolds, S., Teixeira, P.A.G., Blum, A. and Plakas, C. (2017). Unfolded Cylindrical Projection for Rib Fracture Diagnosis . In International Workshop and Challenge on Computational Methods and Clinical Applications in Musculoskeletal Imaging (pp. 36-47). Springer, Cham.


Blobel, J., Mews, J., Goatman, K.A., Schuijf, J.D. and Overlaet, W. (2016). Calibration of coronary calcium scores determined using iterative image reconstruction (AIDR 3D) at 120, 100, and 80 kVp . Medical physics, 43(4), pp.1921-1932.

Jimenez-Del-Toro O, Muller H, Krenn M, Gruenberg K, Taha AA, Winterstein M, Eggel I, Foncubierta-Rodriguez A, Goksel O, Jakab A, Kontokotsios G, Langs G, Menze BH, Salas Fernandez T, Schaer R, Walleyo A, Weber MA, Dicente Cid Y, Gass T, Heinrich M, Jia F, Kahl F, Kechichian R, Mai D, Spanier AB, Vincent G, Wang C, Wyeth D, and Hanbury A. (2016). Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks . IEEE transactions on medical imaging, 35(11), pp.2459-2475.

Mohr, B., Brink, M., Oostveen, L.J., Schuijf, J.D. and Prokop, M., 2016, March. Lung iodine mapping by subtraction with image registration allowing for tissue sliding . In Medical Imaging 2016: Image Processing (Vol. 9784, p. 978442). International Society for Optics and Photonics.

Nakatsugawa, M., Cheng, Z., Goatman, K.A., Lee, J., Robinson, A., Choflet, A., Sakaue, K., Sugiyama, S.,Kiess, A.P., Wong, J.W., McNutt, T.R., Quon, H. (2016) Radiomic Analysis of Salivary Glands and Its Role for Predicting Xerostomia in Irradiated Head and Neck Cancer Patients . International Journal of Radiation Oncology, vol. 96, no. 2, p. S217.

O’Neil, A., Dabbah, M. and Poole, I., 2016, October. Cross-Modality Anatomical Landmark Detection Using Histograms of Unsigned Gradient Orientations and Atlas Location Autocontext . In: International Workshop on Machine Learning in Medical Imaging (MICCAI). LNCS, vol. 6893, pp. 139-146. Springer, Cham.

Rosmini, S., Treibel, T.A., Bandula, S., Stroud, T., Fontana, M., Hawkins, P.N. and Moon, J.C. (2016). Cardiac computed tomography for the detection of cardiac amyloidosis . Journal of cardiovascular computed tomography, 11(2), pp.155-156.


Fuchs, A., Kühl, J.T., Chen, M.Y., Helqvist, S., Razeto, M., Arakita, K., Steveson, C., Arai, A.E. and Kofoed, K.F. (2015). Feasibility of coronary calcium and stent image subtraction using 320-detector row CT angiography . Journal of cardiovascular computed tomography, 9(5), pp.393-398.

Lisowska, A., Wheeler, G., Inza, V. C., & Poole, I. (2015). An Evaluation of Supervised, Novelty-Based and Hybrid Approaches to Fall Detection Using Silmee Accelerometer Data .Proceedings of the IEEE International Conference on Computer Vision , 2015Dec, 402–408.

O'Neil, A., Murphy, S. and Poole, I. (2015). Anatomical Landmark Detection in CT Data by Learned Atlas Location Autocontext . In MIUA (pp. 189-194).

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.

Tang, Q., Matthews, J., Razeto, M., Linde, J.J. and Nakanishi, S. (2015). Motion estimation and compensation for coronary artery and myocardium in cardiac CT . In Medical Imaging 2015: Physics of Medical Imaging (Vol. 9412, p. 94120Q). International Society for Optics and Photonics.

Wang, C., Goatman, K.A., MacGillivray, T., Beveridge, E., Koutraki, Y., Boardman, J., Stirrat, C., Sparrow, S., Moore, E., Paraky, R. and Alam, S. (2015). Automatic multi-parametric MR registration method using mutual information based on adaptive asymmetric k-means binning . In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) (pp. 1089-1092).


Dabbah, M.A., Murphy, S., Pello, H., Courbon, R., Beveridge, E., Wiseman, S., Wyeth, D. and Poole, I. (2014). Detection and location of 127 anatomical landmarks in diverse CT datasets . In Medical Imaging 2014: Image Processing (Vol. 9034, p. 903415). International Society for Optics and Photonics.

Goatman, K., Plakas, C., Schuijf, J., Beveridge, E. and Prokop, M. (2014). Computed tomography lung iodine contrast mapping by image registration and subtraction. In Medical Imaging 2014: Image Processing (Vol. 9034, p. 90343I). International Society for Optics and Photonics.

Murphy, S., Mohr, B., Fushimi, Y., Yamagata, H. and Poole, I. (2014). Fast, simple, accurate multi-atlas segmentation of the brain . In International Workshop on Biomedical Image Registration (pp. 1-10). Springer, Cham.

O'Neil, A., Beveridge, E., Houston, G., McCormick, L. and Poole, I. (2014). Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans . In Medical Imaging 2014: Image Processing (Vol. 9034, p. 90342S). International Society for Optics and Photonics.

Tang, Q., Chiang, B., Akinyemi, A., Zamyatin, A., Shi, B. and Nakanishi, S., 2014, March. A combined local and global motion estimation and compensation method for cardiac CT . In Medical Imaging 2014: Physics of Medical Imaging (Vol. 9033, p. 903304). International Society for Optics and Photonics.

Teixeira, P.A.G., Hossu, G., Lecocq, S., Razeto, M., Louis, M. and Blum, A. (2014) ‘ Bone Marrow Edema Pattern Identification in Patients With Lytic Bone Lesions Using Digital Subtraction Angiography-Like Bone Subtraction on Large-Area Detector Computed Tomography ’. Investigative Radiology, vol. 49, no. 3, pp. 156-164


Kirişli, H.A., Schaap, M., Metz, C.T., Dharampal, A.S., Meijboom, W.B., Papadopoulou, S.L., Dedic, A., Nieman, K., De Graaf, M.A., Meijs, M.F.L. and Cramer, M.J. (2013). Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography . Medical image analysis, 17(8), pp.859-876.


Akinyemi, A., Plakas, C., Piper, J., Roberts, C., & Poole, I. (2012). Optimal Atlas Selection Using Image Similarities in a Trained Regression Model to Predict Performance . 2012 9th IEEE International Symposium on Biomedical Imaging (Isbi) , 1264–1267.

Dickie, D.A., Job, D.E., Poole, I., Ahearn, T.S., Staff, R.T., Murray, A.D. and Wardlaw, J.M., 2012. Do brain image databanks support understanding of normal ageing brain structure? A systematic review . European radiology, 22(7), pp.1385-1394.

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 .

Murphy, S., Akinyemi, A., Steel, J., Petillot, Y., & Poole, I. Multi-compartment heart segmentation in CT angiography using a spatially varying Gaussian classifier . International Journal of Computer Assisted Radiology and Surgery 829–836 (2012).

Piper, J., Ikeda, Y., Fujisawa, Y., Ohno, Y., Yoshikawa, T., O’Neil, A. and Poole, I., 2012. Objective evaluation of the correction by non-rigid registration of abdominal organ motion in low-dose 4D dynamic contrast-enhanced CT . Physics in Medicine & Biology, 57(6), p.1701.


Hernandez, M., Murphy, S., & Wardlaw, J. (2010). A comparison of four unsupervised clustering algorithms for segmenting brain tissue in multi-spectral MR data . In BIOSTECH.


Akinyemi, A., Murphy, S., Poole, I., & Roberts, C. (2009). Automatic labelling of coronary arteries . In European Signal Processing Conference.