Dr Ali Gooya

Senior Lecturer in Machine Learning (School of Computing Science)

telephone: 0141 330 1637
email: Ali.Gooya@glasgow.ac.uk

Glasgow, Glasgow City, Scotland, United Kingdom, Sir Alwyn William Building, G12 8QN

Biography

Ali Gooya is currently a Senior Lecturer in the School of Computing Science (IDA-Section) at the University of Glasgow, Scotland, UK. Before joining the University of Glasgow in 2022, he was an Associate Professor in the School of Computing at the University of Leeds (2018-2022) and the University of Sheffield (2016-2018). He has won multiple prestigious fellowships, including Allen Touring Institute (2022), JSPS short-term invitational (2020), FP7 Marie-Curie IIF fellowship (2014), and Japan Society for Promotion of Science JSPS-PDRA (2008). He earned his PhD from the University of Tokyo in medical image analysis (2007). He joined the University of Pennsylvania as a post-doc research associate to develop machine learning methods for medical vision (2008-2011).

Research interests

My research interest broadly lies in machine learning, computer vision and medical imaging. I am particularly keen on deep probabilistic learning with the target applications in cancer and cardiac image analysis, computer-aided decision support systems, prediction and marker discovery, statistical inference on populations, and computational anatomy.

My research vision is to aspire to unsupervised machine learning for AI in healthcare, as expert annotations in this particular field are sparse. I am experienced in creating methodologically innovative deep Bayesian frameworks, often involving rigorous mathematical modelling.

Publications

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Number of items: 61.

2024

Zakeri, A., Hokmabadi, A., Nix, M. G., Gooya, A. , Wijesinghe, I. and Taylor, Z. A. (2024) 4D-Precise: learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections. Computer Methods and Programs in Biomedicine, 250, 108158. (doi: 10.1016/j.cmpb.2024.108158) (PMID:38604010)

Godson, L., Alemi, N., Nsengimana, J., Cook, G. P., Clarke, E. L., Treanor, D., Bishop, D. T., Newton-Bishop, J., Gooya, A. and Magee, D. (2024) Immune subtyping of melanoma whole slide images using multiple instance learning. Medical Image Analysis, 93, 103097. (doi: 10.1016/j.media.2024.103097) (PMID:38325154)

2023

Elhaminia, B., Gilbert, A., Lilley, J., Abdar, M., Frangi, A. F., Scarsbrook, A., Appelt, A. and Gooya, A. (2023) Toxicity prediction in pelvic radiotherapy using multiple instance learning and cascaded attention layers. IEEE Journal of Biomedical and Health Informatics, 27(4), pp. 1958-1966. (doi: 10.1109/JBHI.2023.3238825)

Zakeri, A., Hokmabadi, A., Bi, N., Wijesinghe, I., Nix, M. G., Petersen, S. E., Frangi, A. F., Taylor, Z. A. and Gooya, A. (2023) DragNet: learning-based deformable registration for realistic cardiac MR sequence generation from a single frame. Medical Image Analysis, 83, 102678. (doi: 10.1016/j.media.2022.102678) (PMID:36403308)

2022

Appelt, A.L., Elhaminia, B., Gooya, A. , Gilbert, A. and Nix, M. (2022) Deep learning for radiotherapy outcome prediction using dose data – a review. Clinical Oncology, 34(2), e87-e96. (doi: 10.1016/j.clon.2021.12.002) (PMID:34924256)

Zakeri, A., Hokmabadi, A., Ravikumar, N., Frangi, A. F. and Gooya, A. (2022) A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment. Medical Image Analysis, 75, 102276. (doi: 10.1016/j.media.2021.102276) (PMID:34753021)

2020

Kumar, N. et al. (2020) A multi-organ nucleus segmentation challenge. IEEE Transactions on Medical Imaging, 39(5), pp. 1380-1391. (doi: 10.1109/TMI.2019.2947628) (PMID:31647422)

2019

Burgos, N., Gooya, A. and Svoboda, D. (2019) Preface. In: Burgos, N., Gooya, A. and Svoboda, D. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (11827). Springer, v-vi. ISBN 9783030327774

Alemi Koohbanani, N., Jahanifar, M., Gooya, A. and Rajpoot, N. (2019) Nuclear instance segmentation using a proposal-free spatially aware deep learning framework. In: Shen, D., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P.-T. and Khan, A. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Series: Lecture Notes in Computer Science, 11764. Springer: Cham, pp. 622-630. ISBN 9783030322380 (doi: 10.1007/978-3-030-32239-7_69)

Attar, R. et al. (2019) Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation. Medical Image Analysis, 56, pp. 26-42. (doi: 10.1016/j.media.2019.05.006) (PMID:31154149)

Zhang, L., Gooya, A. , Pereanez, M., Dong, B., Piechnik, S.K., Neubauer, S., Petersen, S.E. and Frangi, A.F. (2019) Automatic assessment of full left ventricular coverage in cardiac cine magnetic resonance imaging with Fisher-discriminative 3-D CNN. IEEE Transactions on Biomedical Engineering, 66(7), pp. 1975-1986. (doi: 10.1109/TBME.2018.2881952)

Ravikumar, N., Gooya, A. , Beltrachini, L., Frangi, A.F. and Taylor, Z.A. (2019) Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data. Medical Image Analysis, 53, pp. 47-63. (doi: 10.1016/j.media.2019.01.001) (PMID:30684740)

Jahanifar, M., Zamani Tajeddin, N., Mohammadzadeh Asl, B. and Gooya, A. (2019) Supervised saliency map driven segmentation of lesions in dermoscopic images. IEEE Journal of Biomedical and Health Informatics, 23(2), pp. 509-518. (doi: 10.1109/JBHI.2018.2839647) (PMID:29994323)

Attar, R., Pereañez, M., Gooya, A. , Albà, X., Zhang, L., Piechnik, S.K., Neubauer, S., Petersen, S.E. and Frangi, A.F. (2019) High throughput computation of reference ranges of biventricular cardiac function on the UK biobank population cohort. In: Pop, M., Sermesant, M., Zhao, J., Li, S., McLeod, K., Young, A., Rhode, K. and Mansi, T. (eds.) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Series: Lecture Notes in Computer Science, 11395. Springer: Cham, pp. 114-121. ISBN 9783030120283 (doi: 10.1007/978-3-030-12029-0_13)

Fehri, H., Gooya, A. , Lu, Y., Meijering, E., Johnston, S.A. and Frangi, A.F. (2019) Bayesian polytrees with learned deep features for multi-class cell segmentation. IEEE Transactions on Image Processing, 28(7), pp. 3246-3260. (doi: 10.1109/TIP.2019.2895455)

2018

Gooya, A. , Goksel, O., Oguz, I. and Burgos, N. (2018) Preface. In: Gooya, A., Oguz, I., Goksel, O. and Burgos, N. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (11037). Springer, v-vi. ISBN 9783030005351

Nemat, H., Fehri, H., Ahmadinejad, N., Frangi, A.F. and Gooya, A. (2018) Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features. Medical Physics, 45(9), pp. 4112-4124. (doi: 10.1002/mp.13082) (PMID:29974971)

Koohababni, N.A., Jahanifar, M., Gooya, A. and Rajpoot, N. (2018) Nuclei detection using mixture density networks. In: Shi, Y., Suk, H.-I. and Liu, M. (eds.) Machine Learning in Medical Imaging. Series: Lecture Notes in Computer Science, 11046. Springer: Cham, pp. 241-248. ISBN 9783030009182 (doi: 10.1007/978-3-030-00919-9_28)

Gooya, A. , Lekadir, K., Castro-Mateos, I., Pozo, J.M. and Frangi, A.F. (2018) Mixture of probabilistic principal component analyzers for shapes from point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), pp. 891-904. (doi: 10.1109/TPAMI.2017.2700276) (PMID:28475045)

Suinesiaputra, A. et al. (2018) Statistical shape modeling of the left ventricle: myocardial infarct classification challenge. IEEE Journal of Biomedical and Health Informatics, 22(2), pp. 503-515. (doi: 10.1109/JBHI.2017.2652449) (PMID:28103561) (PMCID:PMC5857476)

Ravikumar, N., Gooya, A. , Çimen, S., Frangi, A.F. and Taylor, Z.A. (2018) Group-wise similarity registration of point sets using Student's t-mixture model for statistical shape models. Medical Image Analysis, 44, pp. 156-176. (doi: 10.1016/j.media.2017.11.012) (PMID:29248842)

2017

Kalaie, S. and Gooya, A. (2017) Vascular tree tracking and bifurcation points detection in retinal images using a hierarchical probabilistic model. Computer Methods and Programs in Biomedicine, 151, pp. 139-149. (doi: 10.1016/j.cmpb.2017.08.018) (PMID:28946995)

Tsaftaris, S. A., Gooya, A. , Frangi, A. F. and Prince, J. L. (Eds.) (2017) Simulation and synthesis in medical imaging: Second international workshop, SASHIMI 2017 held in conjunction with MICCAI 2017 Québec city, QC, Canada, September 10, 2017 proceedings. Series: Lecture notes in computer science. Springer. ISBN 9783319681269

Tsaftaris, S.A., Gooya, A. , Frangi, A.F. and Prince, J.L. (2017) Preface. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F. and Prince, J.L. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (10557). Springer, v-vi. ISBN 9783319681269

Zhang, L., Gooya, A. and Frangi, A.F. (2017) Semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets. In: Tsaftaris, S. A., Gooya, A., Frangi, A. F. and Prince, J. L. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (10557). Springer, pp. 61-68. ISBN 9783319681269 (doi: 10.1007/978-3-319-68127-6_7)

Asl, M.E., Koohbanani, N.A., Frangi, A.F. and Gooya, A. (2017) Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform. Journal of Medical Imaging, 4(3), 034006. (doi: 10.1117/1.JMI.4.3.034006) (PMID:28924571) (PMCID:PMC5594385)

Ravikumar, N., Gooya, A. , Frangi, A.F. and Taylor, Z.A. (2017) Generalised coherent point drift for group-wise registration of multi-dimensional point sets. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D. L. and Duchesne, S. (eds.) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Series: Lecture Notes in Computer Science, 10433. Springer: Cham, pp. 309-316. ISBN 9783319661810 (doi: 10.1007/978-3-319-66182-7_36)

Shaukat, F., Raja, G., Gooya, A. and Frangi, A.F. (2017) Fully automatic detection of lung nodules in CT images using a hybrid feature set. Medical Physics, 44(7), pp. 3615-3629. (doi: 10.1002/mp.12273) (PMID:28409834)

Fehri, H., Gooya, A. , Johnston, S.A. and Frangi, A.F. (2017) Multi-class image segmentation in fluorescence microscopy using polytrees. In: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.-T. and Shen, D. (eds.) Information Processing in Medical Imaging. Series: Lecture Notes in Computer Science, 10265. Springer: Cham, pp. 517-528. ISBN 9783319590493 (doi: 10.1007/978-3-319-59050-9_41)

2016

Sarrami-Foroushani, A., Lassila, T., Gooya, A. , Geers, A.J. and Frangi, A.F. (2016) Uncertainty quantification of wall shear stress in intracranial aneurysms using a data-driven statistical model of systemic blood flow variability. Journal of Biomechanics, 49(16), pp. 3815-3823. (doi: 10.1016/j.jbiomech.2016.10.005) (PMID:28573970)

Cimen, S., Gooya, A. , Ravikumar, N., Taylor, Z.A. and Frangi, A.F. (2016) Reconstruction of coronary artery centrelines from X-ray angiography using a mixture of student’s t-distributions. In: Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G. and Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III. Series: Lecture notes in computer science (9902). Springer, pp. 291-299. ISBN 9783319467252 (doi: 10.1007/978-3-319-46726-9_34)

Ravikumar, N., Gooya, A. , Cimen, S., Frangi, A.F. and Taylor, Z.A. (2016) A multi-resolution T-mixture model approach to robust group-wise alignment of shapes. In: Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G. and Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. Series: Lecture notes in computer science (9902). Springer, pp. 142-149. ISBN 9783319467252 (doi: 10.1007/978-3-319-46726-9_17)

Sarrami-Foroushani, A., Lassila, T., Pozo, J.M., Gooya, A. and Frangi, A.F. (2016) Direct estimation of wall shear stress from aneurysmal morphology: a statistical approach. In: Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G. and Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. Series: Lecture notes in computer science (9902). Springer, pp. 201-209. ISBN 9783319467252 (doi: 10.1007/978-3-319-46726-9_24)

Frangi, A.F., Taylor, Z.A. and Gooya, A. (2016) Precision Imaging: more descriptive, predictive and integrative imaging. Medical Image Analysis, 33, pp. 27-32. (doi: 10.1016/j.media.2016.06.024) (PMID:27373145)

Tsaftaris, S.A., Gooya, A. , Frangi, A.F. and Prince, J.L. (2016) Preface. In: Tsaftaris, S. A., Gooya, A., Frangi, A. F. and Prince, J. L. (eds.) Simulation and Synthesis in Medical Imaging: First International Workshop, SASHIMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings. Series: Lecture notes in computer science (9968). Springer, V-VI. ISBN 9783319466293

Zhang, L., Gooya, A. , Dong, B., Hua, R., Petersen, S.E., Medrano-Gracia, P. and Frangi, A.F. (2016) Automated quality assessment of cardiac MR images using convolutional neural networks. In: Tsaftaris, S. A., Gooya, A., Frangi, A. F. and Prince, J. L. (eds.) Simulation and Synthesis in Medical Imaging. Series: Lecture notes in computer science (9968). Springer, pp. 138-145. ISBN 9783319466293 (doi: 10.1007/978-3-319-46630-9_14)

Cimen, S., Gooya, A. , Grass, M. and Frangi, A.F. (2016) Reconstruction of coronary arteries from X-ray angiography: a review. Medical Image Analysis, 32, pp. 46-68. (doi: 10.1016/j.media.2016.02.007) (PMID:27054277)

Cimen, S., Gooya, A. and Frangi, A.F. (2016) Reconstruction of Coronary Artery Centrelines from X-ray Rotational Angiography Using a Probabilistic Mixture Model. In: SPIE Medical Imaging 2016, San Diego, CA, United States, 1–3 March 2016, ISBN 9781510600195 (doi: 10.1117/12.2217116)

Ravikumar, N., Gooya, A. , Frangi, A.F. and Taylor, Z.A. (2016) Robust Group-Wise Rigid Registration of Point Sets Using T-Mixture Model. In: SPIE Medical Imaging 2016, San Diego, CA, United States, 1–3 March 2016, ISBN 9781510600195 (doi: 10.1117/12.2216244)

Peng, P., Lekadir, K., Gooya, A. , Shao, L., Petersen, S.E. and Frangi, A.F. (2016) A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magnetic Resonance Materials in Physics, Biology and Medicine, 29(2), pp. 155-195. (doi: 10.1007/s10334-015-0521-4) (PMID:26811173) (PMCID:PMC4830888)

Pinto, C., Cimen, S., Gooya, A. , Lekadir, K. and Frangi, A.F. (2016) Joint clustering and component analysis of spatio-temporal shape patterns in myocardial infarction. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M. and Young, A. (eds.) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Series: Lecture notes in computer science (9534). Springer, pp. 171-179. ISBN 9783319287119 (doi: 10.1007/978-3-319-28712-6_19)

2015

Shamekhi, S., Miran Baygi, M.H., Azarian, B. and Gooya, A. (2015) A novel multi-scale Hessian based spot enhancement filter for two dimensional gel electrophoresis images. Computers in Biology and Medicine, 66, pp. 154-169. (doi: 10.1016/j.compbiomed.2015.07.010) (PMID:26409228)

Shamekhi, S., Miran Beygi, M.H., Azarian, B. and Gooya, A. (2015) A novel spot-enhancement anisotropic diffusion method for the improvement of segmentation in two-dimensional gel electrophoresis images, based on the watershed transform algorithm. Iranian Journal of Medical Physics, 12(3), pp. 209-222. (doi: 10.22038/IJMP.2015.6222)

Gooya, A. , Lekadir, K., Alba, X., Swift, A.J., Wild, J.M. and Frangi, A.F. (2015) Joint clustering and component analysis of correspondenceless point sets: application to cardiac statistical modeling. In: Ourselin, S., Alexander, D. C., Westin, C.-F. and Cardoso, M. J. (eds.) Information Processing in Medical Imaging: 24th International Conference, IPMI 2015, Sabhal Mor Ostaig, Isle of Skye, UK, June 28 - July 3, 2015, Proceedings. Series: Lecture notes in computer science (9123). Springer, pp. 98-109. ISBN 9783319199917 (doi: 10.1007/978-3-319-19992-4_8)

Gooya, A. , Davatzikos, C. and Frangi, A.F. (2015) A Bayesian approach to sparse model selection in statistical shape models. SIAM Journal on Imaging Sciences, 8(2), pp. 858-887. (doi: 10.1137/140982039)

2014

Samsami, M.M., Firoozabadi, S.M.P. and Gooya, A. (2014) A Morphological Approach for Mental Fatigue Assessment. In: 2013 20th Iranian Conference on Biomedical Engineering (ICBME), Tehran, Iran, 18-20 December 2013, ISBN 9781479932320 (doi: 10.1109/ICBME.2013.6782225)

2013

Gooya, A. , Mousavi, E., Davatzikos, C. and Liao, H. (2013) A Bayesian approach for construction of sparse statistical shape models using Dirichlet distribution. In: Liao, H., Linte, C. A., Masamune, K., Peters, T. M. and Zheng, G. (eds.) Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions. Series: Lecture notes in computer science (8090). Springer, pp. 144-152. ISBN 9783642408427 (doi: 10.1007/978-3-642-40843-4_16)

2012

Gooya, A. , Liao, H. and Sakuma, I. (2012) Generalization of geometrical flux maximizing flow on Riemannian manifolds for improved volumetric blood vessel segmentation. Computerized Medical Imaging and Graphics, 36(6), pp. 474-483. (doi: 10.1016/j.compmedimag.2012.04.007) (PMID:22664135)

Gooya, A. , Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R. and Davatzikos, C. (2012) GLISTR: Glioma image segmentation and registration. IEEE Transactions on Medical Imaging, 31(10), pp. 1941-1954. (doi: 10.1109/TMI.2012.2210558) (PMID:22907965) (PMCID:PMC4371551)

2011

Davatzikos, C., Zacharaki, E.I., Gooya, A. and Clark, V. (2011) Multi-Parametric Analysis and Registration of Brain Tumors: Constructing Statistical Atlases and Diagnostic Tools of Predictive Value. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August - 3 September 2011, ISBN 9781457715891 (doi: 10.1109/IEMBS.2011.6091764)

Gooya, A. , Pohl, K.M., Bilello, M., Biros, G. and Davatzikos, C. (2011) Joint segmentation and deformable registration of brain scans guided by a tumor growth model. In: Fichtinger, G., Martel, A. and Peters, T. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011. Series: Lecture notes in computer science (6892). Springer, pp. 532-540. ISBN 9783642236280 (doi: 10.1007/978-3-642-23629-7_65)

Gooya, A. , Biros, G. and Davatzikos, C. (2011) Deformable registration of glioma images using em algorithm and diffusion reaction modeling. IEEE Transactions on Medical Imaging, 30(2), pp. 375-390. (doi: 10.1109/TMI.2010.2078833) (PMID:20876010) (PMCID:PMC3245665)

2010

Batmanghelich, N., Gooya, A. , Kanterakis, S., Taskar, B. and Davatzikos, C. (2010) Application of Trace-Norm and Low-Rank Matrix Decomposition for Computational Anatomy. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, San Francisco, CA, USA, 13-18 June 2010, ISBN 9781424470303 (doi: 10.1109/CVPRW.2010.5543596)

Gooya, A. , Biros, G. and Davatzikos, C. (2010) An EM Algorithm for Brain Tumor Image Registration: A Tumor Growth Modeling Based Approach. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, San Francisco, CA, USA, 13-18 June 2010, p. 3. ISBN 9781424470303 (doi: 10.1109/CVPRW.2010.5543440)

2008

Gooya, A. , Dohi, T., Sakuma, I. and Liao, H. (2008) R-PLUS: A Riemannian anisotropic edge detection scheme for vascular segmentation. In: Metaxas, D., Axel, L., Fichtinger, G. and Székely, G. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008. Series: Lecture notes in computer science (5241). Springer, pp. 262-269. ISBN 9783540859871 (doi: 10.1007/978-3-540-85988-8_32)

Gooya, A. , Dohi, T., Sakuma, I. and Liao, H. (2008) Anisotropic haralick edge detection scheme with application to vessel segmentation. In: Dohi, T., Sakuma, I. and Liao, H. (eds.) Medical Imaging and Augmented Reality: 4th International Workshop Tokyo, Japan, August 1-2, 2008, Proceedings. Series: Lecture notes in computer science (5128). Springer, pp. 430-438. ISBN 9783540799818 (doi: 10.1007/978-3-540-79982-5_47)

Gooya, A. , Liao, H., Matsumiya, K., Masamune, K., Masutani, Y. and Dohi, T. (2008) A variational method for geometric regularization of vascular segmentation in medical images. IEEE Transactions on Image Processing, 17(8), pp. 1295-1312. (doi: 10.1109/TIP.2008.925378) (PMID:18632340)

2007

Gooya, A. , Liao, H., Matsumiya, K., Masamune, K. and Dohi, T. (2007) Effective statistical edge integration using a flux maximizing scheme for volumetric vascular segmentation in MRA. In: Karssemeijer, N. and Lelieveldt, B. (eds.) Information Processing in Medical Imaging: 20th International Conference, IPMI 2007, Kerkrade, The Netherlands, July 2-6, 2007, Proceedings. Series: Lecture notes in computer science. Springer, pp. 86-97. ISBN 9783540732723 (doi: 10.1007/978-3-540-73273-0_8)

Gooya, A. , Hongen, L., Matsumiya, K., Masamune, K. and Dohi, T. (2007) A Shape Induced Anisotropic Flow for Volumetric Vascular Segmentation in MRA. In: 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 12-15 April 2007, pp. 664-667. ISBN 1424406714 (doi: 10.1109/ISBI.2007.356939)

2006

Gooya, A. , Liao, H., Matsumiya, K., Masamune, K. and Dohi, T. (2006) Pulsative flow segmentation in MRA image series by AR modeling and em algorithm. In: Yang, G.-Z., Jiang, T., Shen, D., Gu, L. and Yang, J. (eds.) Medical Imaging and Augmented Reality: Third International Workshop, Shanghai, China, August 17-18, 2006, Proceedings. Series: Lecture notes in computer science. Springer, pp. 356-363. ISBN 9783540372202 (doi: 10.1007/11812715_45)

Gooya, A. , Liao, H., Matsumiya, K., Masamune, K. and Dohi, T. (2006) Flow Segmentation in Phase Contrast MRA Image Series Using Autoregressive Modeling and Generalized EM Algorithm. CARS 2006 pp. 466-467.

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Number of items: 61.

Articles

Kumar, N. et al. (2020) A multi-organ nucleus segmentation challenge. IEEE Transactions on Medical Imaging, 39(5), pp. 1380-1391. (doi: 10.1109/TMI.2019.2947628) (PMID:31647422)

Book Sections

Edited Books

Conference or Workshop Item

Conference Proceedings

This list was generated on Wed Nov 20 19:11:30 2024 GMT.

Grants

EPSRC Impact Acceleration Award (as PI)
EPSRC New Investigator Grant (as PI, EP/S012796/1)
2 x Cancer Research UK sandpit awards on early detection of cancer
Innovate UK KTP application (as Co-PI)
Marie-Curie IIF Fellowship
JSPS Short Invitational Fellowship
JSPS PDRA Fellowship
2x NVIDIA GPU grant in aid

Supervision

I am looking for PhD students interested in Deep Learning, Probabilistic and Generative Modelling applied to Medical Imaging and patient meta-data. Potential project directions are listed below, and other projects are due. The candidate is expected to have strong analytical and math skills, good programming experience, some prior experience in machine learning and visual computing, and good English communication skills. Please get in touch with me (ali.gooya@glasgow.ac.uk) for further information.

Bayesian Deep Atlases for Cardiac Motion Abnormality Assessment by Integrating Imaging and Metadata

Cardiovascular diseases (CVDs) are the second biggest killer in the UK; currently, more than 7 million people live with CVD in the country. Early identification of individuals with significant risk is critical to improve the patient's quality of life and reduce the financial burden on the social and healthcare systems. Many CVDs lead to a shortage of blood supply to the heart muscle, and abnormal motion is diagnosed non-invasively by analysing the patient's dynamic cardiac imaging data. Manual assessment of these images is subjective, non-reproducible, limited to the left ventricle, and time-consuming. Statistical atlases, describing the 'average' pattern of the heart motion over a sizeable healthy population, can be potentially helpful in identifying deviations from normality in individuals. However, the integration of the existing atlases into clinical practice is inhibited by three fundamental limitations: (i) the derived motion statistics are often independent of the patient's age, gender, weight, etc. (metadata) that are essential for precise diagnosis, (ii) the detected abnormalities due to failure of heart segmentation could not be disentangled from the underlying clinical conditions.

To alleviate these fundamental limitations, this proposal aims, for the first time, to develop a complete probabilistic atlas to evaluate bi-ventricular motion abnormalities accurately.

Holistically integrating imaging and metadata from a large population cardiac imaging study.
Disentangling the algorithmic segmentation failures from underlying clinical conditions
Addressing the computational challenge of extending deep transformer models to motion data

The framework will be a novel Bayesian approach extending the recent developments in deep recurrent neural networks (e.g. Vision Transformers). These networks provide a natural mechanism to model sequential data such as 2D video. Yet, using Transformers to model the complex dynamics of the heart motion is conceptually new and powerful. The motion will be modelled as the spatiotemporal (3D+t) sequence of the heart shapes across the cardiac cycle, extracted from cine Cardiac Magnetic Resonance (CMR) images. The atlas will be a recurrent model that, given a sequence, will predict a probabilistic distribution function (pdf) for the following heart status. The critical aspect is that the pdf will be conditioned on the patient's metadata (age, gender, ethnicity, etc.). Thus by measuring the spatial deviations from the expected shape at each phase, the atlas will allow very accurate quantification of anatomical and functional cardiac abnormalities (and variances showing uncertainties) specific to the patients.

We have extensive experience developing Bayesian and non-Gaussian statistical atlases from cardiac shapes and motion. However, the previous work discarded the patient metadata (such as age, gender, ethnicity, etc.). Therefore, the atlas was not clinically deployable to study cardiac motion abnormalities, which are relevant to various CVDs.

The atlas will be derived from the UK Biobank CMR study aiming to scan 100,000 patients by 2022. The training of the atlas will be pursued as the new releases of the data sets from the UK Biobank becomes available. We have established collaboration with this study's clinical advisor and have full access to the CMR data sets.

Akbari Movahed, Reza
Bayesian Deep Atlases for Cardiac Motion Abnormality Assessment by Integrating Imaging and Metadata
Luo, Zeqi
Human Motion Analysis for Early Screening of Cerebral Palsy

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COMPSCI1020 How to learn a new language?

COMPSCI4015 Professional Software Development

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2022: Short-Term, Tokyo Women Medical University (JSPS Invitational Fellowship)
2019: Early Detection Seed Grants (Cancer Research UK)
2020: Vet-AI/University of Leeds (Innovate-UK)

Research fellowships

2021 - 2022: Allen Touring Institue
2014 - 2016: Marie Skłodowska-Curie Individual Fellowship
2008 - 2010: JSPS Fellowship

Editorial boards

2017 - 2019: MICCAI-SASHIMI Workshop

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Dr Ali Gooya

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