Machine Learning Research

Hrvoje Bogunovic, PhD

Director of Christian Doppler Lab for Artificial Intelligence in Retina
Associate Director and a Technical Lead of Laboratory for Ophthalmic Image Analysis

Department of Ophthalmology and Optometry
Medical University of Vienna
Email: hrvoje.bogunovic(at)meduniwien.ac.at
Phone: +43 1 40400 73419
Office: AKH level 08, room 08.i9.17

Curriculum Vitae: CV
[Google Scholar]

Bio:

Hrvoje Bogunovic obtained his BSc and MSc in Computer Science from the University of Zagreb, Croatia. He obtained his PhD in 2012 from the Universitat Pompeu Fabra (UPF), Barcelona, Spain. For his thesis he worked on medical image segmentation and shape analysis applied to blood vessels of the brain imaged with various angiographic modalities. After graduation he did a postdoc at the Iowa Institute for Biomedical Imaging (IIBI), University of Iowa, US, specializing in medical image analysis for applications in ophthalmology. He moved to Medical University of Vienna, Austria in 2015 to join Christian Doppler Lab for Ophthalmic Image Analysis as lead of computational imaging methods and machine learning. As of 2018 he is a Tenure-Track Faculty at the Medical University of Vienna.

His general research interests are in medical image analysis, imaging genetics, computer vision and machine learning with applications in healthcare. He is particularly interested in machine learning for predicting disease progression and in knowledge discovery from large clinical longitudinal imaging and genetic datasets.


Research Interests:
– Medical Image Computing
– Computational Retinal Image Analysis
– Machine Learning for Healthcare


Selected Publications:

Journal articles
D. Romo-Bucheli, U. Schmidt-Erfurth, H. Bogunovic: End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal OCT imaging. IEEE Journal of Biomedical and Health Informatics, 2020. [DOI]

D. Romo-Bucheli, P. Seeböck, JI Orlando, BS Gerendas, SM Waldstein, U Schmidt-Erfurth, H Bogunovic: Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina. Biomedical Optics Express, 2020. [DOI]

H. Bogunovic, F. Venhuizen, S. Klimscha, S. Apostolopoulos, A. Bab-Hadiashar, U. Bagci, M.F. Beg, L. Bekalo, Q. Chen, C. Ciller, K. Gopinath, A.K. Gostar, K. Jeon, Z. Ji, S. Ho Kang, D. Koozekanani, D. Lu, D. Morley, K.K. Parhi, H. Suk Park, A. Rashno, M. Sarunic, S. Shaikh, J. Sivaswamy, R. Tennakoon, S. Yadav, S. De Zanet, S.M. Waldstein, B.S. Gerendas, C. Klaver, C.I. Sanchez, U. Schmidt-Erfurth. RETOUCH -The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge. IEEE Transactions on Medical Imaging, 2019. [DOI]

U. Schmidt-Erfurth, S. M. Waldstein, S. Klimscha, A. Sadeghipour, X. Hu, B. S. Gerendas, A. Osborne, H. Bogunovic: Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence. Investigative Ophthalmology and Visual Science, 2018. [DOI]

H. Bogunovic, A. Montuoro, M. Baratsits, M.G. Karantonis, S.M. Waldstein, F. Schlanitz and U. Schmidt-Erfurth: Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging. Investigative Ophthalmology and Visual Science, vol. 58 (6), pp. BIO141-BIO150, 2017. [DOI]

H. Bogunovic, S.M. Waldstein, T. Schlegl, G. Langs, A. Sadeghipour, X. Liu, B.S. Gerendas, A. Osborne, U. Schmidt-Erfurth: Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach. Investigative Ophthalmology and Visual Science, vol. 58 (7), pp. 3240-3248, 2017. [DOI]

A. Montuoro, S.M. Waldstein, B.S. Gerendas, U. Schmidt-Erfurth, H. Bogunovic: Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context. Biomedical Optics Express, vol. 8(3), pp. 1874-1888, 2017. [DOI]

H. Bogunovic, Y. H. Kwon, A. Rashid, K. Lee, D. Brice Critser, M. K. Garvin, M. Sonka, and M. D. Abràmoff: Relationships of Retinal Structure and Humphrey 24-2 Visual Field Thresholds in Patients with Glaucoma. Investigative Ophthalmology and Visual Science. vol. 56 (1), pp. 259-271, 2015. [DOI]

H. Bogunovic, M. Sonka, Y. H. Kwon, P. Kemp, M. D. Abràmoff, and X. Wu: Multi-Surface and Multi-Field Co-Segmentation of 3-D Retinal Optical Coherence Tomography. IEEE Transactions on Medical Imaging, vol. 33 (12), pp. 2242-2253, 2014. [DOI] [PDF]

H. Bogunovic, J. M. Pozo, R. Cárdenes, L. San Román, and A. F. Frangi: Anatomical Labeling of the Circle of Willis using Maximum A Posteriori Probability Estimation. IEEE Transactions on Medical Imaging, vol. 32 (9), pp. 1587-1599, 2013. [DOI] [PDF]

H. Bogunovic, J. M. Pozo, R. Cárdenes, M. C. Villa-Uriol, R. Blanc, M. Piotin, and A. F. Frangi: Automated Landmarking and Geometric Characterization of the Carotid Siphon. Medical Image Analysis, vol. 16 (4), pp. 889-903, 2012. [DOI] [PDF]

H. Bogunovic, J. M. Pozo, M. C. Villa-Uriol, C. B. L. M. Majoie, R. van den Berg, H. A. F. Gratama van Andel, J. M. Macho, J. Blasco, L. San Román, and A. F. Frangi: Automated Segmentation of Cerebral Vasculature with Aneurysms in 3DRA and TOF-MRA using Geodesic Active Regions: An Evaluation Study. Medical Physics, vol. 38 (1), pp. 210–222, 2011. [DOI] [PDF]

Conference articles
R. Asgari, J.I. Orlando, S. Waldstein, F. Schlanitz, M. Baratsits, U. Schmidt-Erfurth, H. Bogunovic. Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography.  In International Conference on Medical Image Computing and Computer Assisted Intervention – MICCAI, 2019. [DOI]

A. Rivail, U. Schmidt-Erfurth, W-D Vogl, S.M. Waldstein, S. Riedl, C. Grechenig, Z. Wu, H. Bogunovic. Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning. International Workshop on PRedictive Intelligence In MEdicine – PRIME@MICCAI, 2019. [DOI]

H. Bogunovic, A. Montuoro, S. M. Waldstein, M. Baratsits, F. Schlanitz, and U. Schmidt-Erfurth: Predicting Drusen Regression from OCT in Patients with Age-Related Macular Degeneration. In Proceedings of the 3rd MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA), pp 41-48, Athens, Greece, Oct. 21, 2016. [DOI]

H. Bogunovic, M. D. Abràmoff, and M. Sonka: Geodesic Graph Cut based Retinal Fluid Segmentation in Optical Coherence Tomography. In Proceedings of the 2nd MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA), pp 49-56, Munich, Germany, Oct. 09, 2015. [DOI]

H. Bogunovic, M. D. Abràmoff, L. Zhang, and M. Sonka: Prediction of Treatment Response from Retinal OCT in Patients with Exudative Age-Related Macular Degeneration. In Proceedings of the 1st MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA), pp. 129–136, Boston, USA, Sep. 14, 2014. [DOI]

H. Bogunovic, J. M. Pozo, R. Cárdenes, and A. F. Frangi: Anatomical labeling of the anterior circulation of the Circle of Willis using maximum a posteriori classification. In 14th International Conference on Medical Image Computing and Computer Assisted Intervention – MICCAI, LNCS vol. 6893, pp. 330-337, Toronto, Canada, Sep. 18-22, 2011. [DOI] [PDF]

H. Bogunovic, J. M. Pozo, R. Cárdenes, and A. F. Frangi: Automatic Identification of Internal Carotid Artery from 3DRA Images. In Proceedings of 32nd International Conference of the IEEE Engineering in Medicine and Biology Society – EMBC, pp. 5343–5346, Buenos Aires, Argentina, Aug. 31-Sep. 4, 2010. [DOI] [PDF]

K. Krissian, H. Bogunovic, J. M. Pozo, M. C. Villa-Uriol and A. F. Frangi: Minimally Interactive Knowledge-based Coronary Tracking in CTA using a Minimal Cost Path. In The MIDAS Journal – Grand Challenge Coronary Artery Tracking (MICCAI 2008 Workshop),  New York, USA, Sep. 9, 2008. [HDL] [PDF]

H. Bogunovic, A. Radaelli, M. De Craene, D. Delgado and A. F. Frangi: Image Intensity Standardization in 3D Rotational Angiography and its Application to Vascular Segmentation. In Proceedings of SPIE Medical Imaging 2008: Image Processing, Article 691419, San Diego, USA, Feb. 16-21, 2008. [DOI] [PDF]

H. Bogunovic and S. Loncaric: Blood Flow and Velocity Estimation Based on Vessel Transit Time by Combining 2D and 3D X-Ray Angiography. In 9th International Conference on Medical Image Computing and Computer Assisted Intervention – MICCAI, LNCS vol. 4191, pp. 117-124, Copenhagen, Denmark, Oct. 01-06, 2006. [DOI] [PDF]

H. Bogunovic and S. Loncaric: Estimating Perfusion Using X-Ray Angiography. In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis – ISPA, pp 147-150, Zagreb, Croatia, Sep. 15-17, 2005. [DOI] [PDF]

H. Bogunovic and S. Loncaric: Denoising of Time-Density Data in Digital Subtraction Angiography. In 14th Scandinavian Conference on Image Analysis – SCIA, LNCS vol. 3540, pp. 1157-1166, Joensuu, Finland, June 19-22, 2005. [DOI] [PDF]

H. Bogunovic and S. Loncaric: Optical Flow Estimation of the Heart Motion using Line Process. In Proceedings of the 4th IASTED International Conference on Biomedical Engineering – BioMED, pp. 296-299, Innsbruck, Austria, Feb. 16-18, 2005. [PDF]

PhD Thesis
H. Bogunovic: Geometric Modeling and Characterization of the Circle of Willis. Universitat Pompeu Fabra, Barcelona, Spain, 2012. [PDF]

MSc Thesis
H. Bogunovic: Blood Flow Analysis from Angiogram Image Sequence. University of Zagreb, Croatia, 2005. [PDF]