Research
Image Quality Assessment
We aim to develop algorithms, that are capable to automatically detecting low quality medical images using deep learning techniques. The success of such image quality techniques can increase of the image analysis pipelines dramatically and overcome the tedious labelling task.
Selection of Publications
Ozer C., Oksuz, I., Explainable Image Quality Analysis of Chest X-Rays, MIDL (oral acceptance rate < %15), 2021 (oral presentation). Online
Oksuz, I., Ruijsink B., Puyol-Anton E., Sinclair M., Rueckert D., Schnabel J., King A.P., Automatic Left Ventricular Outflow Tract Classification For Accurate Cardiac MR Planning, ISBI, 2018, (Oral Presentation) Online
Faster Image Acquisition
We aim to develop algorithms, that are capable to generate robust image reconstructions using deep learning techniques. The success of such image reconstruction techniques can enable faster image acquisitions and reduce the MR scan times dramatically. This can reduce the average cost of an MRI scan without reducing the diagnostic quality of final images. The deployment of such techniques in clinical setup can generate and efficient pipeline for image acquisition.
Selection of Publications
Schlemper, J., Oksuz, I., Clough, J. R., Duan, J., King, A. P., Schnabel, J. A., ... & Rueckert, D. (2019). dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance. Online
Fuin N., Bustin A., Kuestner T., Oksuz, I., Clough J., King A.P., Schnabel J.A., Botnar R., Prieto C., A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography, Magnetic Resonance Imaging (IF: 2.053), 2020. Online
End-to-End Image Analysis
End-to-end deep learning frameworks can be used as a global image reconstructors. Our goal is to generate high quality data to address the tasks of artefact correction and downstream segmentation. Our fundamental task is image artefact detection, correction and segmentation jointly, resulting in a network architecture that can output both good quality image reconstructions and segmentations. We aim to have a single framework for clinical use, which can be used for task specific image acquisition.
Selection of Publications
Oksuz, I., Clough J., Ruijsink B., Puyol-Anton E., Bustin A., Cruz G.,, Prieto C., King A.P., Schnabel J.A., Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation, IEEE TMI (accepted, IF: 7.816), 2020. Online
Oksuz, I., Clough J., Ruijsink B., Puyol-Anton E., Bustin A.,Cruz G., Prieto C., Rueckert D., King A.P., Schnabel J.A., Detection and Correction of Cardiac MRI Motion Artefacts during Reconstruction from k-space, MICCAI (oral acceptance rate < %5), 2019 (oral presentation). Online
Oksuz, I., Clough J., Bai W., Ruijsink B., Puyol-Anton E., Cruz G., Prieto C., King A.P., Schnabel J.A., High-quality segmentation of low quality cardiac MR images using k-space artefact correction, MIDL (acceptance rate < %40), 2019. Online
Electricity Price Forecasting
We aim to develop accurate machine learning models to enable sensitive predictions in time series data. The fundamental goal is to address the challenging the nature of electricity price data due to high volatility and sharp spikes. Our main application fields are the intraday and day-ahead electricity price markets.
Journal Publications
Karagoz, A., Alis, D., Seker, M.E, Zeybel G., Yergin M., Oksuz, I., Karaarslan, E., Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study. Insights Imaging, 2023. Online
Alis, D., Kartal, M. S., Seker, M. E., Guroz, B., Basar, Y., Arslan, A.,Sirolu S., Kurtcan S., Denizoglu N., Tuzun U., Yldrm D., Oksuz, I., Karaarslan, E.. Deep Learning for Assessing Image Quality in Bi-Parametric Prostate MRI: A Feasibility Study. European Journal of Radiology 2023. Online
Deari S., Oksuz, I., Ulukaya S.,Block Attention and Switchable Normalization based Deep Learning Framework for Segmentation of Retinal Vessels,IEEE Access 2023. Online
Li, L., Wu F., Wang S., Luo X., Martn-Isla C., Zhai S., Zhang J., Liu Y., Zhang Z., Ankenbrand M.J., Jiang H., Zhang X., Wang L., Arega T.W., Altunok E., Zhao Z., Li F., Ma J., Yang X., Puybareau E., Oksuz, I., Bricq S., Li W., Punithakumar K., Tsaftaris S.A., Schreiber L.M., Yang G., Liu G., Xia Y., Wang G., Escalera S., Zhuang X., MyoPS: a benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images.” Medical Image Analysis 2023. Online
Bagcilar, O., Alis, D., Alis, C., Seker, M. E., Yergin, M., Ustundag, A., Hikmet E., Tezcan A., Polat G., Akkus A.T., Alper F., Velioglu M., Yildiz O., Selcuk H.H., , Oksuz, I., Kizilkilic O., Karaarslan, E. Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study. Scientific Reports 13, 8834, 2023. Online
Yakut C., Ulukaya S., Oksuz, I., A Hybrid Fusion Method Combining Spatial Image Filtering with Parallel Channel Network for Retinal Vessel Segmentation, Arabian Journal for Science and Engineering, 2023 Online
Gunduz S., Ugurlu U., Oksuz, I., Transfer Learning for Electricity Price Forecasting, Sustainable Energy, Grids and Networks 2023. Arxiv Online
Clough J., Byrne N, Oksuz, I., Zimmer V.A., Schnabel J.A., King A.P., A topological loss function for deep-learning based image segmentation using persistent homology, IEEE PAMI, 2022. Online Arxiv
Thomas C., Dregely I., Oksuz, I., Guerrero-Urbano T., Greener A., King A.P., Barrington S., Neural-Network Dose-Prediction for Rectal Spacer Stratification in DoseEscalated Prostate Radiotherapy, Medical Physics 2022. Online
Alis D., Alis C., Yergin M., Topel C., Asmakutlu O., Bagcilar O., Senli Y., Ustundag A., Salt V., Dogan S., Velioglu M., Selcuk H., Kara B., Ozer C., Oksuz, I., Kizilkilic O., Karaarslan E., A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on non-contrast head CT: A multicenter study, Nature Scientific Reports 2022. Online
Bohlender S., Oksuz, I., Mukhopadhyay A., A survey on shape-constraint deep learning for medical image segmentation, IEEE Reviews in Biomedical Engineering, 2022. Online Arxiv
Soyak R., Ersoy E.A., Navruz E., Cruz G., Prieto C., King A.P., Unay D., Oksuz, I., Channel Attention Networks for Robust MR Fingerprint Matching, IEEE Transactions on Biomedical Engineering 2021. Online
Alis D., Yergin M., Alis C., Topel C., Asmakutlu O., Bagcilar O., Senli Y., Ustundag A., Salt V., Dogan S., Velioglu M., Selcuk H., Kara B., Oksuz, I., Kizilkilic O., Karaarslan E., Inter-Vendor Performance of Deep Learning in Segmenting Acute Ischemic Lesions on Diffusion-Weighted Imaging: A Multicenter Study, Nature Scientific Reports . Online
Humbert-Vidan L., Patel V., Oksuz, I., King A.P., Urbano T.G., Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer, The British Journal of Radiology, 2021. Online
Xue W., Li, J., Hu Z., Kerfoot E., Clough J.E., Oksuz, I., Xu H., Grau V., Gu, F., Ng M., Li S., Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-ventricular Short-axis Cardiac MR Data, IEEE JBHI, 2021. Online
Oksuz, I., Brain MRI artefact detection and correction using convolutional neural networks, Computer Methods and Programs in Biomedicine, 2021, Online
Oksuz, I., Clough J., Ruijsink B., Puyol-Anton E., Bustin A., Cruz G.,, Prieto C., King A.P., Schnabel J.A., Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation, IEEE TMI, 2020. Online
Fuin N., Bustin A., Kuestner T., Oksuz, I., Clough J., King A.P., Schnabel J.A., Botnar R., Prieto C., A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography, Magnetic Resonance Imaging, 2020. Online
Ali S, Zhou F, Braden B, Bailey A, Yang S, Cheng G, Zhang P, Li X, Kayser M, Soberanis-Mukul RD, Albarqouni S., Wang X., Wang C., Watanabe S., Oksuz I., Ning Q., Yang S., Khan M.A., Gao X.W., Realdon S., Loshchenov S., Schnabel J.A., East J.E., Wagnieres G., Loschenov V.B., Grisan E., Daul C., Blondel W., Rittscher J., An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy. Scientific Reports. 2020. Online
Oksuz, I., Ugurlu, U., Neural Network Based Model Comparison for Intraday Electricity Price Forecasting, Energies, 2019. Online
Ruijsink B., Puyol-Anton E., Oksuz, I., Sinclair M.,Bia W., Schnabel J.A., Rezavi R., King A.P., Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function, Journal of the American College of Cardiology , 2019. Online
Yang H.S., Oksuz, I., Rey D., , Sykes J., Klein M. , Butler J. , Kovacs M.S. , Sobczyk V. , Cokic I. , Slomka P.J. , Bi X. , Li D., Tighiouart M. , Prato F.S. , Tsaftaris S.A: , Fisher J.A. , Dharmakumar R., Accurate Needle-Free Assessment of Myocardial Oxygenation for Ischemic Heart Disease, Science Translational Medicine , 2019. Online
Oksuz, I., Ruijsink B., Puyol-Anton E., Clough J., Cruz G., Bustin A., Botnar R., Prieto C., Rueckert D., Schnabel J.A., King A.P., Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning, Medical Image Analysis , 2019. Online
Ugurlu, U.*, Oksuz, I.*,Tas, O., Electricity Price Forecasting Using Recurrent Neural Networks, Energies, 2018. Online
Ugurlu, U., Tas, O., Kaya, A., Oksuz, I., The Financial Effect of the Electricity Price Forecasts Inaccuracy on a Hydro-Based Generation Company, Energies, 2018. Online
Oksuz, I., Mukhopadhyay, A., Dharmakumar, R., Tsaftaris, S.A., Unsupervised Myocardial Segmentation for Cardiac BOLD, IEEE TMI, Online
Suinesiaputra A., Ablin P., Alba X.,Alessandrini M.,Allen J., Bai W., Cimen S., Claes P., Cowan P., Dhooge J., Duchateau N., Ehrhardt J., Frangi A.F., Gooya A., Grau V., Lekadir K., Lu A., Mukhopadhyay A., Oksuz I., Parajuli N., Pennec X., Pereanez M., Pinto C., Piras P., Rohe M., Rueckert D., Saring D., Sermesant M., Siddiqi K., Tabassian M., Teresi L., Tsaftaris S.A., Wilms m., Young A.A., Zhang X.,Gracia P.M., Statistical shape modeling of the left ventricle: myocardial infarct classification challenge, IEEE JBHI 2017, Online PDF
Rudyanto, R.D., Kerkstra, S., van Rikxoort, E.M., Fetita, C., Brillet, P., Lefevre, C., Xue, W.,Zhu, X.,Liang, J., Oksuz, I., Unay, D., Kadipasaoglu, K.,Estpar, R.,Ross, J.C., Washko, G. R.,Prieto, J.,Hoyos, Marcela H., Orkisz, M., Meine, H., Hllebrand, M.,Stcker, C.,Mir, F.,Naranjo, V., Villanueva, E., Staring, M., Xiao, C., Stoel, B.C., Fabijanska, A., Smistad, E., Elster, Anne C., Lindseth, F., Foruzan, A., Kiros, R.,Popuri, K.,Cobzas, D., Jimenez-Carretero, D., Santos, A., Ledesma-Carbayo, M.J., Helmberger, M., Urschler, M., Pienn, M., Bosboom, D.G.H., Co, A., Prokop, M.,de Jong, P.A.,Ortiz-de-Solorzano, C., Muoz-Barrutia, A., van Ginneken, B., Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study, Medical Image Analysis, 2014. Online PDF
Kirisli, H.A.,Schaap, M., Metz, C.T., Dharal, A.S., Meijboom, W.B.,Papadopoulou, S. L.,Dedic, A., Nieman, K., de Graaf, M.A., Meijs, M.F.L.,Cramer, M.J.,Broersen, A., Cetin, S., Eslami, A.,Flrez-Valencia, L., Lor, K.L., Matuszewski, B.,Melki, I., Mohr, B., Oksuz, I., Shahzad, R., Wang, C. ,Kitslaar, P.H., Unal, G.,Katouzian, A., Orkisz, M.,Chen, C.M., Precioso, F.,Najman, L., Masood, S., Unay, D., van Vliet, L.,Moreno, R.,Goldenberg, R., Vucini, E., Krestin, G.P., Niessen, W.J., van Walsum, T., Standardized Evaluation Framework for Evaluating Coronary Artery Stenosis Detection, Stenosis Quantification and Lumen Segmentation Algorithms in Computed Tomography Angiography, Medical Image Analysis, 2013. Online PDF
Conference Publications
Ozer, C., Guler, A., Cansever, A. T., Alis, D., Karaarslan, E., Oksuz, I., Shifted Windows Transformers for Medical Image Quality Assessment, MICCAI-MLMI 2022. Link
Ranem A., Kalhof J., Ozer C., Mukhopadhyay A., Oksuz, I., Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation, STACOM-CMRxMOTION 2022. Arxiv
Ruru X., Oksuz, I., Efficient MRI Reconstruction with Reinforcement Learning for Automatic Acquisition Stopping, STACOM 2022. Link
Acar M., Cukur T., Oksuz, I., SSegmentation-Aware MRI Reconstruction, MLMIR 2022. Link
Cetindag S.C., Yergin M., Alis D., Oksuz, I., Meta-learning for Medical Image Segmentation Uncertainty Quantification, Brainles 2022. Link
Sezen G., Oksuz, I., Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning, MICCAI-PRIME 2022. Link
Aydin F., Oksuz, I., Game Character Generation with Generative Adversarial Networks, SIU 2022. Link
Haykir A.A., Oksuz, I., Transfer Learning Based Super Resolution of Aerial Images, SIU 2022. Link
Acar M., Cukur T., Oksuz, I., Self-supervised Dynamic MRI Reconstruction, MLMIR 2021. Online
Özer C., Oksuz, I., Cross-domain artefact correction of cardiac MRI, STACOM 2021. Online Video
Machado I.P., Puyol-Anton E., Hammernik K., Cruz G., Öksüz, İ., Ruijsink B., Young A., Prieto C., Schnabel J.A., King A.P. Quality-aware Cine Cardiac MRI Acquisition and Reconstruction from Undersampled k-space Data to Reduce Scan Time, ESMRMB 2021. Online
Ozer C., Oksuz, I., Explainable Image Quality Analysis of Chest X-Rays, MIDL (oral acceptance rate < %15), 2021 (oral presentation). Online
Deari S., Oksuz, I., Ulukaya S., Importance of Data Augmentation and Transfer Learning on Retinal Vessel Segmentation, TELFOR 2021. Online
Artunc F., Oksuz, I., An Ensemble Approach for Automatic Artefact Detection on Gastroendoscopy Images, UBMK 2021. Online
Bolhassani M., Oksuz, I., Semi-Supervised Segmentation of Multi-vendor and Multi- center Cardiac MRI, SIU 2021. Online
Karakamis K., Ozer C., Oksuz, I., Artifact Detection in Cardiac MRI Data by Deep Learning Methods, SIU 2021. Online
Altunok E., Oksuz, I., Accurate Myocardial Pathology Segmentation with Residual U-Net, STACOM 2020 (oral presentation). Online
Gunduz S., Ugurlu U., Oksuz, I., Electricity Price Prediction Using Encoder-Decoder Recurrent Neural Networks in Turkish Dayahead Market, SIU 2020. Online
Soyak R., Ersoy E.A., Navruz E., Unay D., Oksuz, I., Accurate Dictionary Matching for MR Fingerprinting Using Neural Networks and Feature Extraction, SIU 2020. Online
Cruz G., Kuestner T.,Oksuz, I., Olivier J., Fuin N., King A. P., Schnabel J. A., Botnar R., Prieto C.,Tissue based denoising for MR fingerprinting via long short-term memory networks, International Society of Magnetic Resonance in Medicine Meeting (ISMRM), 2020. Online
Thomas C., Dregely I., Oksuz, I., Guerrero-Urbano T., Greener A., King A.P., Barrington S., Deep learning for rectal spacer stratification in prostate boost radiotherapy, European Radiotherapy and Oncology (ESTRO) Meeting, 2020. Online
Thomas C., Dregely I., Oksuz, I., Guerrero-Urbano T., Greener A., King A.P., Barrington S., Effect of pseudoCT methods on dose-derived rectal toxicity prediction in MR-only prostate RT, European Radiotherapy and Oncology (ESTRO) Meeting, 2020. Online
Humbert-Vidan, L., Oksuz, I.,Patel V. , King A. P., and Guerrero-Urbano T., Prediction of voxelwise mandibular osteoradionecrosis maps in HNC patients using deep learning", European Radiotherapy and Oncology (ESTRO) (2019): S1050. Online
Oksuz, I., Clough J., Ruijsink B., Puyol-Anton E., Bustin A.,Cruz G., Prieto C., Rueckert D., King A.P., Schnabel J.A., Detection and Correction of Cardiac MRI Motion Artefacts during Reconstruction from k-space, MICCAI (oral acceptance rate < %5), 2019 (oral presentation). Online
Clough J., Oksuz, I., Puyol-Anton E., Ruijsink B., Schnabel J.A., King A.P., Global and Local Interpretability for Cardiac MRI Classification, MICCAI (acceptance rate < %35), 2019. Online
Puyol-Anton E., Ruijsink B., Clough J., Oksuz, I., Rueckert D., Rezavi R., King A.P., Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders, MICCAI-STACOM, 2019. Online
Oksuz, I., Clough J., Bai W., Ruijsink B., Puyol-Anton E., Cruz G., Prieto C., King A.P., Schnabel J.A., High-quality segmentation of low quality cardiac MR images using k-space artefact correction, MIDL (acceptance rate < %40), 2019. Online
Clough J., Oksuz, I., Bryne N., Schnabel J.A., King A.P., Explicit topological priors for deep-learning based image segmentation using persistent homology, IPMI (acceptance rate < %25), 2019. Online
Gomez, A., Schmitz, C., Henningsson, M., Housden, J., Noh, Y., Zimmer, V.A., Clough, J.R., Oksuz, I., Toussaint, N., King, A.P. and Schnabel, J.A., 2019. Mechanically Powered Motion Imaging Phantoms: Proof of Concept. EMBC 2019. Online
Oksuz, I., Cruz G., Clough J., Bustin A., Nicolo F., Botnar R.M., Prieto C., King A.P., Schnabel J.A., Magnetic Resonance Fingerprinting using Recurrent Neural Networks, ISBI (acceptance rate < %40), 2019. Online
Oksuz, I., Clough J., Puyol-Anton E., Bustin A., Cruz G., Prieto C., Botnar R., Rueckert D., Schnabel J., King A.P., Cardiac MR Motion Artefact Correction from K-space using Deep Learning-based Reconstruction, MLMIR (MICCAI), 2018, PDF
Oksuz, I., Ruijsink B., Puyol-Anton E., Bustin A., Cruz G., Prieto C., Rueckert D., Schnabel J., King A.P., Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection, MICCAI, 2018, PDF
Oksuz, I., Ruijsink B., Puyol-Anton E., Sinclair M., Rueckert D., Schnabel J., King A.P., Automatic Left Ventricular Outflow Tract Classification For Accurate Cardiac MR Planning, ISBI, 2018, (Oral Presentation) Preprint Online
Oksuz, I., A., Dharmakumar, R., Tsaftaris, S.A., Joint Myocardial Registration and Segmentation of Cardiac BOLD MRI, STACOM 2017, (Best paper award), PDF Online
Yang H.S.*, Oksuz I.*, Klein M., Sobczyk O., Dey D., Sykes J., Butler J., Bi X., Sharif B., Cokic I., Li, D., Slomka D.,Prato F.S., Fisher J., Tsaftaris S.A., Dharmakumar R., Cardiac fMRI - A Novel Approach for Reliably Detecting Myocardial Oxygenation Changes with Precise Modulation of Arterial CO2, International Society of Magnetic Resonance in Medicine Meeting (ISMRM), 2017.(accepted for power pitch presentation) PDF
Oksuz, I., Dharmakumar, R., Tsaftaris S.A., Fully automated myocardial segmentation of cardiac BOLD MRI, Society for Cardiovascular Magnetic Resonance Annual Meeting, 2017 (SCMR), 2017. PDF
Onofrey J., Oksuz, I., Sarkar, S.,Venkataraman, R., Staib, L.H., Papademetris X., MRI-TRUS Image Synthesis with Application to Image-Guided Prostate Intervention. International Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI), 2016. PDF
Oksuz, I., Dharmakumar, R., Tsaftaris S.A., Multi-Resolution Registration and Segmentation for cardiac BOLD MRI, International Society of Magnetic Resonance in Medicine Meeting (ISMRM), 2016. (oral presentation)Online PDF
Oksuz, I., Bevilacqua, M., Mukhopadhyay, A., Dharmakumar, R., Tsaftaris, S.A.,BOLD contrast: A challenge for cardiac image analysis , Society for CardiovascularMagnetic Resonance (SCMR) Annual Meeting, 2016 . PDF
Oksuz, I.,Dharmakumar, R., Tsaftaris, S.A., Towards joint segmentation and registration of the myocardium in CP-BOLD MRI at rest,Society for Cardiovascular Magnetic Resonance (SCMR) Meeting, 2016, PDF
Mukhopadhyay, A., Oksuz, I., Tsaftaris, S.A., Supervised Learning of Functional Maps for Infarct Classication, Statistical Atlases and Computational Models of the Heart (STACOM), 2015. PDF
Oksuz, I., Mukhopadhyay, A., Bevilacqua, M., Dharmakumar, R., Tsaftaris, S.A., Dictionary Learning Based Image Descriptor for Myocardial Registration of CP-BOLD MR, Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015. PDF
Mukhopadhyay, A., Oksuz, I., Bevilacqua, M., Dharmakumar, R., Tsaftaris, S.A., Unsupervised myocardial segmentation for cardiac MRI, Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015. PDF
Oksuz, I., Mukhopadhyay, A., Bevilacqua, M., Dharmakumar, R., Tsaftaris, S.A., Data-Driven Feature Learning for Myocardial Segmentation of CP-BOLD MR, Functional Imaging and Modeling of Heart (FIMH), 2015.(oral presentation) PDF
Oksuz, I., Mukhopadhyay, A., Bevilacqua, M., Yang H.J., Dharmakumar, R., Tsaftaris S.A., Effect of BOLD Contrast on Myocardial Registration, International Society of Magnetic Resonance in Medicine Meeting (ISMRM), 2015. PDF
Mukhopadhyay, A., Bevilacqua, M.,Oksuz, I., Dharmakumar, R., Tsaftaris, S.A., Data Driven Feature Learning For Representation of Myocardial BOLD MR Images, International Society of Magnetic Resonance in Medicine Meeting (ISMRM), 2015. PDF
Bevilacqua, M., Mukhopadhyay, A., Oksuz, I., Rusu C., Dharmakumar, R., Tsaftaris S.A., Dictionary-based Support Vector Machines for Unsupervised Ischemia Detection at Rest with CP-BOLD Cardiac MRI, International Society of Magnetic Resonance in Medicine Meeting (ISMRM), 2015. PDF
Oksuz, I., Unay, D., Kadipasaoglu, K., Region Growing on Frangi Vesselness Values in 3-D CTA Data, Proceedings of the 21st Signal Processing and Communications Applications Conference (SIU). IEEE, pp. 1-4. ISBN 978-1-4673-5561-2, 2013. PDF
Unay, D., Harmankaya, I., Oksuz, I., Kadipasaoglu, K., Cubuk, R., Celik, L., Automated aortic supravalvular sinus detection in conventional computed tomography image, In: Proceedings of the 21st Signal Processing and Communications Applications Conference (SIU). IEEE, pp. 1-4. ISBN 978-1-4673-5561-2, 2013. PDF
Oksuz, I., Unay, D., Kadpasaoglu, K., A Hybrid Method for Coronary Artery Stenosis Detection and Quantification in CTA Images, Workshop on 3D Cardiovascular Imaging: A MICCAI Segmentation Chalenge, Proc. 15th Int. Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice - France, 2012. PDF
Oksuz, I., Unay, D., Kadipasaoglu, K., Multi-scale Hessian Based Approach of Lung Vessel Tree in 3-D CTA Data : A ISBI Segmentation Challenge, Proc. of International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), 2012. PDF
Oksuz, I., Unay, D., Kadipasaoglu, K., Segmentation of lung vessel tree in 3-D CTA data, Proc. (MASFOR), Istanbul - Turkey, 2012. PDF