Bagci Lab

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Publication List

Years

2023

  1. Self-supervised Semantic Segmentation: Consistency over Transformation.
    S Karimijafarbigloo, R Azad, et al. ICCV 2023, IEEE International Conference on Computer Vision 2023
    https://arxiv.org/pdf/2309.00143.pdf
  2. Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection.
    Azad, Reza, et al. MICCAI 2023.
    arXiv:2309.00108 (2023).
  3. Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation.
    Azad, Reza, et al. arxiv preprint arXiv:2309.00121 (2023).
    https://doi.org/10.48550/arXiv.2309.00121
  4. Deep Learning Algorithms for Pancreas Segmentation from Radiology Scans: A Review. Zhang, Zheyuan, et al. Advances in Clinical Radiology (2023).
    https://doi.org/10.1016/j.yacr.2023.05.001
  5. A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging. Yao, Lanhong, et al. Current Opinion in Gastroenterology 39.5 (2023): 436-447.
    https://doi.org/10.1097/MOG.0000000000000966
  6. Monkeypox Diagnosis with Interpretable Deep Learning. Ahsan, Md Manjurul, et al. IEEE Access (2023). https://ieeexplore.ieee.org/abstract/document/10198433
  7. Radiomics Boosts Deep Learning Model for IPMN Classification. Yao, Lanhong, et al. MICCAI MLMI 2023.
    http://arxiv.org/abs/2309.05857
  8. AI-Powered Road Network Prediction with Multi-Modal Data. Necip Gengeç et al. Earth Science Informatics. 2023.
    link to be published
  9. Gastrointestinal Disease Diagnosis with Hybrid Model of Capsules and CNNs. Sarsengeldin, Merey, et al. 2023 IEEE International Conference on Electro Information Technology (eIT). IEEE, 2023. https://ieeexplore.ieee.org/abstract/document/10187250
  10. GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection. Jha, Debesh, et al. ICML Workshop, arXiv preprint arXiv:2307.08140 (2023).
    https://doi.org/10.48550/arXiv.2307.08140
  11. Domain generalization with correlated style uncertainty. Zhang, Zheyuan, et al. WACV 2024, arXiv preprint arXiv:2212.09950 (2022).
    https://doi.org/10.48550/arXiv.2212.09950
  12. GazeGNN: A Gaze-Guided Graph Neural Network for Disease Classification. Wang, Bin, et al. WACV 2024, arXiv preprint arXiv:2305.18221 (2023).
    https://doi.org/10.48550/arXiv.2305.18221
  13. Real-time multi-class helmet violation detection using few-shot data sampling technique and yolov8. Aboah, Armstrong, et al. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.
    link to CVPR open access
  14. Deepsegmenter: Temporal action localization for detecting anomalies in untrimmed naturalistic driving videos. Aboah, Armstrong, et al. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.
    link to CVPR open access
  15. GazeSAM: What you see is what you segment. Wang, Bin, et al. arXiv preprint arXiv:2304.13844 (2023).
    https://doi.org/10.48550/arXiv.2304.13844
  16. A multi-centre polyp detection and segmentation dataset for generalisability assessment. Ali, Sharib, et al. Scientific Data 10.1 (2023): 75.
    https://www.nature.com/articles/s41597-023-01981-y
  17. AI in Clinical Medicine: A Practical Guide for Healthcare Professionals. Michael F. Byrne (Editor), Nasim Parsa (Co-Editor), Alexandra T. Greenhill (Co-Editor), Daljeet Chahal (Co-Editor), Omer Ahmad (Co-Editor), Ulas Bagci (Co-Editor).
    https://onlinelibrary.wiley.com/doi/book/10.1002/9781119790686
  18. Relational reasoning network for anatomical landmarking. Neslisah Torosdagli, Syed Anwar, Payal Verma, Denise K. Liberton, Janice S. Lee, Wade W. Han, Ulas Bagci. J. Med. Imag. 10(2), 024002 (2023),
    https://doi.org/10.1117/1.JMI.10.2.024002
  19. Ensemble Learning with Residual Transformer for Brain Tumor Segmentation. Yao, Lanhong, et al. ISBI, 2023.
    https://doi.org/10.48550/arXiv.2308.00128
  20. TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing. Debesh Jha et al. MIDL 2023.
    https://doi.org/10.48550/arXiv.2303.07428
  21. The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence. Keles, Elif, and Ulas Bagci. arXiv preprint arXiv:2302.00225 (2023).
    https://doi.org/10.48550/arXiv.2302.00225
  22. TransResU-Net: Transformer based ResU-Net for real-time colonoscopy polyp segmentation. Tomar, Nikhil Kumar, et al. arXiv preprint arXiv:2206.08985 (2022).
    https://doi.org/10.48550/arXiv.2206.08985
  23. Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification. Ilkin Isler, Debesh Jha, Curtis Lisle, et al. IEEE ICECCME 2023.
    https://doi.org/10.48550/arXiv.2305.02491
  24. An efficient multi-scale fusion network for 3D organ at risk (OAR) segmentation. Srivastava, Abhishek, et al. arXiv preprint arXiv:2208.07417 (2022).
    https://doi.org/10.48550/arXiv.2208.07417
  25. Deep Learning Prediction of MRI-Induced Power Absorption in Patients with Deep Brain Stimulation Leads. Yalcin Tur, et al. IEEE EMBC 2023 
    link to be published
  26. AI Empowered Automatic Volume Delineation of Liver from CT Scans for Diagnostic Workflow. Ugur Demir, Zheyuan Zhang, Bin Wang, et al. DDW 2023 
    link to be published
  27. A Fully Automatic AI System for Pancreas Segmentation from Multicenter MRI Scans. Zheyuan Zhang, et al. DDW 2023 
    link to be published
  28. DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation. Tomar, NK., Jha, D., Bagci, U. SPIE 2023. https://doi.org/10.48550/arXiv.2210.13595
  29. RUPNet: residual upsampling network for real-time polyp segmentation. Tomar, Nikhil Kumar, Ulas Bagci, and Debesh Jha. Medical Imaging 2023: Computer-Aided Diagnosis. Vol. 12465. SPIE, 2023.
    https://doi.org/10.1117/12.2655126

2022

  1. An automatic segmentation framework for computer-assisted renal scintigraphy. Rahimi, A., Hosntalab, M., Babapour, F., Amoui, M., Bagci, U. Medical & Biological Engineering & Computing, 1-11 (2022).
    DOI: 10.1007/s11517-022-02717-7
  2. Musculoskeletal MR Image Segmentation with Artificial Intelligence. Keles, E., Irmakci, I., Bagci, U. Advances in Clinical Radiology, 4(1), 179-188 (2022).
    https://doi.org/10.1016/j.yacr.2022.04.010
  3. Federated Learning for Medical Applications: A Taxonomy, Current Trends, and Research Challenges. Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. arXiv preprint arXiv:2208.03392 (2022).
    https://doi.org/10.48550/arXiv.2208.03392
  4. Deformable capsules for object detection. Lalonde, R., Khosravan, N., & Bagci, U. arXiv preprint (2022).
    arXiv:2104.05031 
  5. COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers. Aytekin, Idil, et al. arXiv preprint. IEEE JBHI (2022).
    arXiv:2207.09529
  6. An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation. Srivastava, A., Jha, D., Keles, E., Aydogan, B., Abazeed, M., & Bagci, U. arXiv preprint (2022).
    https://doi.org/10.48550/arXiv.2208.07417
  7. Multi-Contrast MRI Segmentation Trained on Synthetic Images. Irmakci, I., Unel, Z. E., Ikizler-Cinbis, N., & Bagci, U. IEEE EMBC 2022.
    arXiv:2207.02469
  8. Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images. Proietto Salanitri, F. et al. IEEE EMBC 2022.
    arXiv e-prints: arXiv-2206
  9. Video Analytics in Elite Soccer: A Distributed Computing Perspective. Jha, D., Rauniyar, A., Johansen, H. D., Johansen, D., Riegler, M. A., Halvorsen, P., & Bagci, U. In 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM) (pp. 221-225) (2022).
    DOI: 10.1109/SAM53842.2022.9827827
  10. TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation. Tomar, N. K., Shergill, A., Rieders, B., Bagci, U., & Jha, D. IEEE MM (2022).
    https://doi.org/10.48550/arXiv.2206.08985
  11. Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network. Srivastava, A., Tomar, N. K., Bagci, U., & Jha, D. IEEE CBMS, arXiv e-prints, arXiv-2206 (2022).
    https://doi.org/10.48550/arXiv.2206.08298
  12. Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network. Tomar, N. K., Srivastava, A., Bagci, U., & Jha, D. arXiv e-prints, arXiv-2206 (2022).
    https://doi.org/10.48550/arXiv.2206.06264
  13. Dynamic Linear Transformer for 3D Biomedical Image Segmentation. Zhang, Z., & Bagci, U. arXiv e-prints, arXiv-2206 (2022).
    https://doi.org/10.48550/arXiv.2206.00771
  14. Transformer-based Generative Adversarial Network for Liver Segmentation. Demir, Ugur, et al. arXiv e-prints: arXiv-2205 (2022).
    https://doi.org/10.48550/arXiv.2205.10663
  15. TGANet: Text-guided attention for improved polyp segmentation. Tomar, N. K., Jha, D., Bagci, U., & Ali, S. MICCAI 2022 (2022).
    https://doi.org/10.48550/arXiv.2205.04280
  16. Detecting COVID-19 from respiratory sound recordings with transformers. Aytekin, I., Dalmaz, O., Ankishan, H., Saritas, E. U., Bagci, U., Cukur, T., & Celik, H. In Medical Imaging 2022: Computer-Aided Diagnosis (Vol. 12033, pp. 25-32) (2022).
    https://doi.org/10.1117/12.2611490
  17. Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem. Marvasti, A. E., Marvasti, E. E., & Bagci, U. arXiv preprint arXiv:2203.12145 (2022).
    https://doi.org/10.48550/arXiv.2203.12145
  18. Enhancing organ at risk segmentation with improved deep neural networks. Isler, I., Lisle, C., Rineer, J., Kelly, P., Turgut, D., Ricci, J., & Bagci, U. In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 814-820) (2022). https://doi.org/10.48550/arXiv.2202.01866

  19. Deep Learning Models in the Classification of Intraductal Papillary Mucinous Neoplasms with Visual Explanation Methods to Highlight Areas of Interest for Algorithm Decision Process. Engels, M. M., et al. Gastroenterology, 162(7), S-127 (2022).
    https://doi.org/10.1016/S0016-5085(22)60308-1

  20. Multi-Institutional Large-Scale Validation of 8 Methods for Automatic Knee MRI Segmentation for Use in Clinical Trials. Dam, E. B., et al. Osteoarthritis and Cartilage, 30, S291-S292 (2022).
    https://doi.org/10.1016/j.joca.2022.02.391

2021

  1. The Role of CO-RADS Scoring System in the Diagnosis of COVID-19 Infection and its Correlation with Clinical Signs.
    Çomoglu, S., Öztürk, S., Topçu, A., Kulali, F., Kant, A., Sobay, R., … & Yilmaz, G. Current Medical Imaging 18.4 (2022): 381-386.
    DOI: 10.2174/1573405617666210827150937
  2. Machine Learning-Based Prediction of MRI-Induced Power Absorption in the Tissue in Patients With Simplified Deep Brain Stimulation Lead Models.
    Vu, J., Nguyen, B. T., Bhusal, B., Baraboo, J., Rosenow, J., Bagci, U., … & Golestanirad, L. (2021). IEEE Transactions on Electromagnetic Compatibility, 63(5), 1757-1766.
    DOI: 10.1109/temc.2021.3106872
  3. A Novel Decision Support System for Long-Term Management of Bridge Networks.
    Karaaslan, E., Bagci, U., & Catbas, N. (2021). Applied Sciences, 11(13), 5928.
    https://doi.org/10.3390/app11135928
  4. Maximum Probability Theorem: A Framework for Probabilistic Machine Learning.
    Marvasti, A. E., Marvasti, E. E., Bagci, U., & Foroosh, H. (2021). IEEE Transactions on Artificial Intelligence, 2(3), 214-227.
    https://doi.org/10.48550/arXiv.1910.09417
  5. Attention-guided Analysis of Infrastructure Damage with Semi-supervised Deep Learning.
    Karaaslan, E., Bagci, U., & Catbas, F. N. (2021). Automation in Construction, 125, 103634.
    https://doi.org/10.1016/j.autcon.2021.103634
  6. Deep Learning Based Staging of Bone Lesions from Computed Tomography Scans.
    Masoudi, S., Mehralivand, S., Harmon, S.A., et al. (2021). IEEE Access, 9, pp.87531-87542.
    DOI: 10.1109/access.2021.3074051
  7. Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players.
    RaviPrakash, H., Anwar, S. M., Biassou, N. M., & Bagci, U. (2021). Frontiers in Neuroscience, 15, 629478.
    https://doi.org/10.3389/fnins.2021.629478
  8. The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset.
    Desai, A. D., Caliva, F., Iriondo, C., et al. (2021). Radiology: Artificial Intelligence, 3(3), e200078.
    https://doi.org/10.1148/ryai.2021200078
  9. A Machine Learning-Based Prediction of the Micropapillary/Solid Growth Pattern in Invasive Lung Adenocarcinoma with Radiomics.
    He, B., Song, Y., Wang, L., et al. (2021). Translational Lung Cancer Research, 10(2), p.955.
    DOI: 10.21037/tlcr-21-44
  10. Capsules for Biomedical Image Segmentation.
    LaLonde, R., Xu, Z., Irmakci, I., Jain, S., & Bagci, U. (2021). Medical image analysis, 68, 101889.
    https://doi.org/10.1016/j.media.2020.101889
  11. Quick Guide on Radiology Image Pre-processing for Deep Learning Applications in Prostate Cancer Research.
    Masoudi, Samira, Stephanie AA Harmon, Sherif Mehralivand, et al. Journal of Medical Imaging 8, no. 1 (2021): 010901.
    https://doi.org/10.1117/1.JMI.8.1.010901
  12. Predicting RF Heating of Conductive Leads During Magnetic Resonance Imaging at 1.5 T: A Machine Learning Approach.
    Zheng, C., Chen, X., Nguyen, B. T., et al. (2021). In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 4204-4208). IEEE.
    DOI: 10.1109/EMBC46164.2021.9630718
  13. Hierarchical 3D Feature Learning for Pancreas Segmentation.
    Proietto Salanitri, F., Bellitto, G., Irmakci, I., Palazzo, S., Bagci, U., & Spampinato, C. (2021).  In International Workshop on Machine Learning in Medical Imaging (pp. 238-247). Springer, Cham.
    DOI: 10.48550/arXiv.2109.01667
  14. Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis.
    Demir, U., Irmakci, I., Keles, E., Topcu, A., Xu, Z., Spampinato, C., … & Bagci, U. (2021). In International Workshop on Machine Learning in Medical Imaging (pp. 396-405). Springer, Cham.
    DOI: 10.48550/arXiv.2104.02869
  15. No-Reference Image Quality Assessment Of T2-Weighted Magnetic Resonance Images In Prostate Cancer Patients.
    Masoudi, S., Harmon, S., Mehralivand, S., Lay, N., Bagci, U., Wood, B.J., Pinto, P.A., Choyke, P. and Turkbey, B. (2021). In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 1201-1205). IEEE.
    DOI:10.1109/ISBI48211.2021.9434027
  16. Interpretable Deep Model for Predicting Gene-Addicted Non-Small-Cell Lung Cancer in CT Scans.
    Pino, Carmelo, Simone Palazzo, Francesca Trenta, et al. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 891-894. IEEE, 2021.
    DOI:10.1109/ISBI48211.2021.9433832
  17. Deep Multi-stage Model for Automated Landmarking of Craniomaxillofacial CT Scans.
    Palazzo, Simone, Giovanni Bellitto, Luca Prezzavento, et al. In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9982-9987. IEEE, 2021.
    DOI:10.1109/ICPR48806.2021.9412910
  18. Deep Recurrent-Convolutional Model for Automated Segmentation of Craniomaxillofacial CT Scans.
    Murabito, Francesca, Simone Palazzo, F. Proietto Salanitri, et al. In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9062-9067. IEEE, 2021.
    DOI:10.1109/ICPR48806.2021.9413084
  19. Missed Diagnosis of Pancreatic Ductal Adenocarcinoma Detection Using Deep Convolutional Neural Network.
    Hoogenboom, S. A., Ravi, K., Engels, M. M., Irmakci, I., Keles, E., Bolan, C. W., … & Bagci, U. (2021). Gastroenterology, 160(6), S-18.
    link to Google Scholar

2020

  1. AI for the detection of COVID19 pneumonia on chest CT using multinational datasets.
    Stephanie A. Harmon, Thomas H. Sanford, Sheng Xu, et al. Nature Communications, 2020.
    https://doi.org/10.1038/s41467-020-17971-2
  2. Semi-supervised deep learning for multi-tissue segmentation from multi-contrast MRI.
    Anwar, Syed Muhammad, Ismail Irmakci, Drew A. Torigian, Sachin Jambawalikar, Georgios Z. Papadakis, Can Akgun, Jutta Ellermann, Mehmet Akcakaya, and Ulas Bagci. Journal of Signal Processing Systems (2020): 1-14.
    https://doi.org/10.1007/s11265-020-01612-4
  3. Integrating eye tracking and speech recognition accurately annotates MR brain images for deep learning: proof of principle.
    Stember, Joseph N., Haydar Celik, David Gutman, Nathaniel Swinburne, Robert Young, Sarah Eskreis-Winkler, Andrei Holodny et al. Radiology: Artificial Intelligence 3, no. 1 (2020): e200047.
    https://doi.org/10.1148/ryai.2020200047
  4. The impact of COVID-19 on African American communities in the United States.
    Cyrus, Elena, Rachel Clarke, Dexter Hadley, Zoran Bursac, Mary Jo Trepka, Jessy G. Dévieux, Ulas Bagci et al. Health Equity 4, no. 1 (2020): 476-483.
    https://doi.org/10.1089/heq.2020.0030
  5. Proceedings from the first global artificial intelligence in gastroenterology and endoscopy Summit.
    Parasa, S., Wallace, M., Bagci, U., Antonino, M., Berzin, T., Byrne, M., Celik, H., Farahani, K., Golding, M., Gross, S., and Jamali, V. Gastrointestinal endoscopy, 92(4), pp.938-945.
    https://doi.org/10.1016/j.gie.2020.04.044
  6. EEG based classification of long-term stress using psychological labeling.
    Saeed, Sanay Muhammad Umar, Syed Muhammad Anwar, Humaira Khalid, Muhammad Majid, and Ulas Bagci. Sensors 20, no. 7 (2020): 1886.
    https://doi.org/10.3390/s20071886
  7. Development of a Device-to-Image Registration Free Needle Guide for Magnetic Resonance Imaging-Guided Targeted Prostate Biopsy.
    Pankaj Kulkarni, Sakura Sikander, Pradipta Biswas, Sumit Laha, Heather Cornnell, Jeremy R Burt, Ulas Bagci, Sang-Eun Song. Journal of Medical Devices 14(4).
    https://doi.org/10.1115/1.4047874
  8. PET/CT and PET/MRI in Ophthalmic Oncology.
    Kalemaki MS, Karantanas AH, Exarchos D, Detorakis ET, Zoras O, Marias K, Millo C, Bagci U, Pallikaris I, Stratis A, Karatzanis I, Perisinakis K, Koutentakis P, Kontadakis GA, Spandidos DA, Tsatsakis A, Papadakis GZ. International Journal of Oncology.
    https://doi.org/10.3892/ijo.2020.4955
  9. AI in Gastroenterology. Current State of Play and Potential. How will it affect our practice and when?
    Hoogenboom, S., Bagci, U., Wallace, MB. Techniques in Gastrointestinal Endoscopy.
    https://doi.org/10.1016/j.tgie.2019.150634
  10. Analysis of Video Retinal Angiography with Deep Learning and Eulerian Magnification.
    Saha, S., LaLonde R., Carmack, A., Foroosh, H., Olson, J.C., Shaikh, S., Bagci, U. Frontiers in Computer Science.
    https://doi.org/10.3389/fcomp.2020.00024
  11. Deep Learning Provides Exceptional Accuracy to ECoG-based Functional Language Mapping for Epilepsy Surgery.
    RaviPrakash, H., Korostenskaja, M., Castillo, E., Lee, KH., Salinas, CM., Baumgartner, J., Scampinatoe, C., Bagci, U. Frontiers in Neuroscience.
    https://doi.org/10.3389/fnins.2020.00409
  12. State-of-the-art in brain tumor segmentation and current challenges.
    Yousaf, Sobia, Harish RaviPrakash, Syed Muhammad Anwar, Nosheen Sohail, and Ulas Bagci. In Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-Oncology, pp. 189-198. Springer, Cham, 2020.
    https://link.springer.com/chapter/10.1007/978-3-030-66843-3_19
  13. Deep Convolutional Neural Networks Based Classification of Alzheimer’s Disease Using MRI Data.
    Nawaz, A., Anwar, S., Liaqat, R., Iqbal, J., Bagci, U. IEEE INMIC 2020.
    https://doi.org/10.48550/arXiv.2101.02876
  14. Encoding High-Level Visual Attributes in Capsules for Explainable Medical Diagnoses.
    LaLonde, R., Torigian, D., Bagci, U. MICCAI 2020.
    https://doi.org/10.48550/arXiv.1909.05926
  15. Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features.
    Shaheen, A., Bagci, U., Mohy-ud-Din, H. RNO-AI (Radiomics and Radiogenomics in Neuro-oncology), MICCAI 2020.
    https://link.springer.com/chapter/10.1007/978-3-030-66843-3_25
  16. Brain Tumor Survival Prediction using Radiomics Features.
    Yousaf, S., Anwar, S, RaviPrakash, H., Bagci, U. RNO-AI (Radiomics and Radiogenomics in Neuro-oncology), MICCAI 2020.
    https://doi.org/10.48550/arXiv.2009.02903
  17. Diagnosing Colorectal Polyps in the Wild with Capsule Networks.
    LaLonde, R., Kandel, P., Spampinato, C., Wallace, M, Bagci, U. IEEE ISBI 2020.
    https://doi.org/10.48550/arXiv.2001.03305
  18. Instance-level Microtubule Tracking.
    Masoodi, S., Razi, A., Wright, C., Gatlin, J., Bagci, U. IEEE Transactions on Medical Imaging.
    https://doi.org/10.1109/TMI.2019.2963865
  19. Variational Capsule Encoder.
    RaviPrakash, H., Anwar, SM., Bousquet, C., Bagci, U. ICPR 2020.
    https://doi.org/10.48550/arXiv.2010.09102
  20. Adipose Tissue Segmentation in Unlabeled Abdomen MRI Using Cross Modality Domain Adaptation.
    Masoudi, S., Razi, A., Gatlin, J., Turkbey, B., Bagci, U. IEEE EMBC 2020.
    DOI: 10.1109/EMBC44109.2020.9176009

 

 

2019

  1. Artificial Intelligence as Another Set of Eyes in Breast Cancer Diagnosis.
    Anwar, SM, and Bagci, U. Journal of Medical Artificial Intelligence, 2019.
    http://dx.doi.org/10.21037/jmai.2019.04.03
  2. Gold Standard for Epilepsy/Tumor Surgery Coupled with Deep Learning Offers Independence to a Promising Functional Mapping Modality.
    Korostenskaja, M., RaviPrakash, H., Bagci, U., … , Elsayed, M., and Castillo, E. Brain-Computer Interface Research, A State-of-the-Art Summary 7, Springer, 2019.
    https://doi.org/10.1007/978-3-030-05668-1_2
  3. Deep Learning for Functional Brain Connectivity: Are We There Yet?
    RaviPrakash, R., Watane, A., Jambawalikar, S., and Bagci, U. Springer (Deep Learning and CNN for Medical Image Computing, Invited), 2019.
    https://doi.org/10.1007/978-3-030-13969-8_17
  4. Lung Imaging and CADx. Chapter 14: How Deep Learning is Changing the Landscape of Lung Cancer Diagnosis.
    Hussein, S., and Bagci, U. CRC Press (invited), 2019.
    https://doi.org/10.1201/9780429055959
  5. Computerized Analysis of Brain MR parameters dynamics in young patients with Cushing-Syndrome – a case control study.
    Tirosh, A., RaviPrakash, H., … , Bagci, U., Stratakis, CA. The Journal of Clinical Endocrinology and Metabolism, 2019.
    https://doi.org/10.1210/clinem/dgz303
  6. RETOUCH-The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.
    Bugunovic, H., … , Gerendas, BS., Klaver, C., Sánchez, CI., Schmidt-Erfurth, U. IEEE Transactions on Medical Imaging, 2019.
    https://doi.org/10.1109/TMI.2019.2901398
  7. Quality Assurance of Computer-Aided Detection and Diagnosis in Colonoscopy.
    Vinsard, DG., … , Wallace, MB. Gastrointestinal Endoscopy, 2019.
    https://doi.org/10.1016/j.gie.2019.03.019
  8. Deep Learning to Classify Intraductal Papillary Mucinous Neoplasms Using Magnetic Resonance Imaging.
    Hurtado, S. Hussein, … , and M. Wallace. Pancreas, 2019.
    https://doi.org/10.1097/MPA.0000000000001327
  9. 18F-NaF PET/CT imaging in fibrous dysplasia of bone: implications for evaluation of disease activity and treatment.
    Papadakis, GZ., … , and Boyce, AM. Journal of Bone and Mineral Research, 2019.
    https://doi.org/10.1002/jbmr.3738
  10. Eye-Tracking for Deep Learning Segmentation Using Convolutional Neural Networks: a proof-of-principle application to meningiomas.
    Stember, JN., … , Jambawalikar, S., Bagci, U. Journal of Digital Imaging, 2019.
    https://doi.org/10.1007/s10278-019-00220-4
  11. Artificial Intelligence Assisted Infrastructure Assessment Using Mixed Reality Systems.
    Karaaslan, E., Bagci, U., and Catbas, F.N. Journal of Transportation Research, 2019.
    https://doi.org/10.1177%2F0361198119839988
  12. Deep Geodesic Learning for Segmentation and Anatomical Landmarking.
    Torosdagli, D. Liberton, … , Bagci, U. IEEE Transactions on Medical Imaging, 2019.
    https://doi.org/10.1109/TMI.2018.2875814
  13. A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye Tracking, Sparse Attentional Model, and Deep Learning.
    Khosravan, N., Celik, H., Turkbey, B., Jones, E., Wood, B., Bagci, U. Medical Image Analysis, 2019.
    https://doi.org/10.1016/j.media.2018.10.010
  14. Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches.
    Hussein, S., Kandel, MM., Bolan, CW., Wallace, MB., Bagci, U. IEEE Transactions on Medical Imaging, 2019.
    https://doi.org/10.1109/TMI.2019.2894349
  15. INN: Inflated Neural Networks for IPMN Diagnosis.
    LaLonde, R., Tanner, I., Nikiforaki, K., Papadakis, GZ., Kandel, P., Bolan, CW., Wallace, M., Bagci, U. MICCAI 2019.
    https://doi.org/10.1007/978-3-030-32254-0_12
  16. PAN: Projective Adversarial Network for Medical Image Segmentation.
    Khosravan, N., Mortazi, A., Wallace, M., Bagci, U. MICCAI 2019.
    https://doi.org/10.1007/978-3-030-32226-7_8
  17. Weakly Supervised Segmentation by A Deep Geodesic Prior.
    Mortazi, A., Khosravan, N., Torigian, DA., Kurugol, S., Bagci, U. MICCAI 2019-MLMI.
    https://doi.org/10.1007/978-3-030-32692-0_28
  18. Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans.
    Liu, Y., Khosravan, N., Liu, Y., Stember, J., Bagci, U., Jambawalikar, S. MICCAI 2019-DART.
    https://doi.org/10.1007/978-3-030-33391-1_8
  19. A Survey on Recent Advancements for AI-Enabled Radiomics in Neuro-Oncology.
    Anwar, S., Tooba, A., Rafique, K., RaviPrakash, H., Mohy-ud-din, H., Bagci, U. MICCAI 2019-RNO-AI.
    https://doi.org/10.1007/978-3-030-40124-5_3
  20. Classification of Perceived Human Stress Using Psychological Signals.
    Arsalan, A., Majid, M., Anwar, SM., Bagci, U. IEEE EMBC.
    https://doi.org/10.1109/JBHI.2019.2926407
  21. Emotion Classification in Response to Tactile Enhanced Multimedia Using Frequency Domain Features of Brain Signals.
    Arsalan, A., Majid, M., Anwar, SM., Bagci, U. IEEE EMBC.
    https://doi.org/10.1109/EMBC.2019.8857632
  22. Deep Learning for Musculoskeletal Image Analysis.
    Irmakci, I., Anwar, S., Torigian, D., Bagci, U. IEEE ASILOMAR 2019 (invited article).
    https://doi.org/10.48550/arXiv.2003.00541
  23. MRI-guided, Transperineal Prostate Biopsy Using Fixed Coordinate Needle Guide: Initial Feasibility Study.
    Kulkarni, P., Laha, S., Sikander, S., Cornell, H., Bagci, U., Burt, J., and Song, SS. Design of Medical Devices Conference, 2019.
    https://doi.org/10.1115/DMD2019-3281
  24. Brown Adipose Tissue Activation Detected by Artificial Intelligence Assisted Radiomics: An Early Biomarker for Pancreatic Ductal Adenocarcinoma (PDAC).
    Rodriguez, AC., Sharma, A., Tanner, I., Kandel, P., Lalonde, R., Livingston, D., Manoj, J., Raimondo, M., Wallace, M., Bagci, U. Gastroenterology.
    https://doi.org/10.1016/S0016-5085(19)37629-2
  25. Colorectcal Polyp Diagnosis With Contemporary Artificial Intelligence.
    Kandel, P., Lalonde, R., Ciofoaia, V., Wallace, M., Bagci, U. Gastrointestinal Endoscopy.
    https://doi.org/10.1016/j.gie.2019.03.613
  26. F-18-NaF Uptake by Fibrous Dysplasia Bone Lesions is Positively Associated with Bone Turnover Markers (BTMs).
    Papadakis, GZ., Manikis, GC., Karantanas, AH., Marias, K., Bagci, U., Florenzano, P., Collins, MT, Boyce, AM. EJNMMI 2019.
    link to be updated
  27. Prognostic Utility of F-18-NaF PET/CT Imaging for Fractures in Patients with Fibrous Dysplasia of Bone.
    Papadakis, GZ., Manikis, GC, Karantanas, AH., Marias, K., Bagci, U., Florenzano, P., Collins, MT, Boyce, AM. EJNMMI 2019.
    link to be updated

2018 & Before

  1. Xu, Z., G. Z. Papadakis, D. J. Mollura, and Bagci, Ulas (2017). Imaging Infection. Chapter 11: Image Analyses. Ed. by S. Jain. Springer.
    https://doi.org/10.1007/978-3-319-54592-9
  2. A.Statnikov, C. Aliferis, D. Hardin, and I. Guyon (2013). A Gentle Introduction to Support Vector Machines in Biomedicine Volume 2: Case Studies and Benchmarks.
    http://www.worldscientific.com/worldscibooks/10.1142/7923
  3. U. Bagci (2010). Automatic Anatomy Recognition and Registration-Medical Image Processing and Analysis. LAP LAMBERT Academic Publishing.
    ISBN-10: 3838380142
  4. Xu, Z., Gao, M., Papadakis, GZ., Luna, B., Jain, S., Mollura, D., Bagci, U (2018). Joint Solution for PET Image Segmentation, Denoising, and Partial Volume Correction. Medical Image Analysis 46, 229-243.
    https://doi.org/10.1016/j.media.2018.03.007
  5. Irmakci, I., Hussein, S., Savran, A., Kalyani, R., Reiter, D., Chia, C., Fishbein, K., Spencer, R., Ferruci, L., Bagci, U. (2018). A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole-Body Tissue Segmentation. IEEE Transactions on Biomedical Engineering, 2018.
    https://doi.org/10.1109/TBME.2018.2866764
  6. Xiang, D., Bagci, U., Jin, C., Shi, F., Zhu, W., Yao, J., Chen, X (2017). CorteXpert: A model-based method for automated renal cortex segmentation. Medical Image Analysis 42, 257-273.
    https://doi.org/10.1016/j.media.2017.06.010
  7. Burt, J., Torosdagli, N., Khosravan, N., RaviPrakash, H., Mortazi, A., Tissavirasingham, F., Hussein, S., Bagci, U. (2018). Deep Learning Beyond Cats and Dogs: Recent Advances in Diagnosing Breast Cancer with Deep Neural Network. British Journal of Radiology 91, 20170545.
    https://doi.org/10.1259/bjr.20170545
  8. Green, A., Bagci, U., S. Hussein, and M. Osman (2017). Brown Adipose Tissue Detected by PET/CT Imaging is Associated with Less Central Obesity. Nuclear Medicine Communication 38(7), 537-539.
    https://doi.org/10.1097/MNM.0000000000000691
  9. M.Buty, Z.Xu, A.Wu, M. Gao, C. Nelson, G. Papadakis, U.Teomete, H. Celik, B. Turkbey, P. Choyke, D. Mollura, U.Bagci, and L. Folio (2017). Quantitative Image Quality Comparison of Reduced and Standard Dose Dual Energy Multiphase Chest, Abdomen, and Pelvis CT. Tomography 3 (2), 114–122.
    https://doi.org/10.18383/j.tom.2017.00002
  10. Papadakis, G. Z., S. Jha, T. Bhattacharyya, C. Millo, T.-W. Tu, Bagci, Ulas, K. Marias, A. H. Karan-tanas, and N. J. Patronas (2017). 18F-NaF PET/CT in Extensive Melorheostosis of the Axial and Appendicular Skeleton With Soft-Tissue Involvement. Clinical Nuclear Medicine 42(7), 537–539.
    https://doi.org/10.1097/RLU.0000000000001647
  11. Papadakis, G. Z., D. Mavroudis, V. Georgoulias, J. Souglakos, A. K. Alegakis, G. Samonis, Bagci, Ulas, A. Makrigiannakis, and O. Zoras (2017). Serum IGF-1, IGFBP-3 levels and circulating tumor cells (CTCs) in early breast cancer patients. Growth Hormone & IGF Research.
    https://doi.org/10.1016/j.ghir.2017.02.001
  12. Papadakis, G. Z., C. Millo, A. H. Karantanas, Bagci, Ulas, and N. J. Patronas (2017). Avascular Necrosis of the Hips With Increased Activity on 68Ga-DOTATATE PET/CT. Clinical Nuclear Medicine.
    https://doi.org/10.1097/RLU.0000000000001513
  13. Papadakis, G. Z., C. Millo, S. M. Sadowski, A. H. Karantanas, Bagci, Ulas, and N. J. Patronas (2017). Breast Fibroadenoma With Increased Activity on 68Ga DOTATATE PET/CT. Clinical Nuclear Medicine 42(2), 145–146.
    https://doi.org/10.1097/RLU.0000000000001463
  14. Papadakis, G. Z., C. Millo, S. M. Sadowski, A. H. Karantanas, Bagci, Ulas, and N. J. Patronas (2017). Fibrous Dysplasia Mimicking Malignancy on 68Ga-DOTATATE PET/CT. Clinical Nuclear Medicine.
    https://doi.org/10.1097/RLU.0000000000001527
  15. Candemir, S., S. Jaeger, S. Antani, Bagci, Ulas, L. R. Folio, Z. Xu, and G. Thoma (2016). Atlas-based rib-bone detection in chest X-rays. Computerized Medical Imaging and Graphics 51, 32–39.
    https://doi.org/10.1016/j.compmedimag.2016.04.002
  16. Hussein, S., A. Green, A. Watane, D. Reiter, X. Chen, G. Z. Papadakis, B. Wood, A. Cypess, M. Osman, and Bagci, Ulas (2016). Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans. IEEE Transactions on Medical Imaging 36(3), 734–744.                                                                                    https://doi.org/10.1109/TMI.2016.2636188
  17. Johnson, R. F. et al. (2016). 3B11-N, a monoclonal antibody against MERS-CoV, reduces lung pathology in rhesus monkeys following intratracheal inoculation of MERS-CoV Jordan-n3/2012. Virology 490, 49–58.                https://doi.org/10.1016/j.virol.2016.01.004
  18. Kopriva, I., W. Ju, B. Zhang, F. Shi, D. Xiang, K. Yu, X. Wang, Bagci, Ulas, and X. Chen (2016). Single-channel Sparse Nonnegative Blind Source Separation Method for Automatic 3D Delineation of Lung Tumor in PET Images. IEEE Journal of Biomedical and Health Informatics.
    https://doi.org/10.1109/JBHI.2016.2624798
  19. Papadakis, G. Z., C. Millo, Bagci, U., Blau, J., and Collins, MT (2016). 18F-NaF and 18F-FDG PET/CT in Gorham-Stout Disease. Clinical Nuclear Medicine 41(11), 884–885.
    https://doi.org/10.1097/RLU.0000000000001369
  20. Papadakis, G. Z., C. Millo, S. M. Sadowski, Bagci, Ulas, and N. J. Patronas (2016). Endolymphatic Sac Tumor Showing Increased Activity on 68Ga DOTATATE PET/CT. Clinical Nuclear Medicine 41(10), 783–784.
    https://doi.org/10.1097/RLU.0000000000001315
  21. Papadakis, G. Z., C. Millo, S. M. Sadowski, Bagci, Ulas, and N. J. Patronas (2016). Epididymal Cystadenomas in von Hippel-Lindau Disease Showing Increased Activity on 68Ga DOTATATE PET/CT. Clinical Nuclear Medicine 41(10), 781–782.
    https://doi.org/10.1097/RLU.0000000000001314
  22. Papadakis, G. Z., C. Millo, S. M. Sadowski, Bagci, Ulas, and N. J. Patronas (2016). Kidney Tumor in a von Hippel-Lindau (VHL) Patient with Intensely Increased Activity on 68Ga-DOTA-TATE PET/CT. Clinical Nuclear Medicine 41(12), 970–971.
    https://doi.org/10.1097/RLU.0000000000001393
  23. Papadakis, G. Z., S. M. Sadowski, Bagci, Ulas, and C. Millo (2016). Application of 68Ga-DOTATATE PET/CT in metastatic neuroendocrine tumor of gastrointestinal origin. Annals of Gastroenterology 30(1), 130.
    https://dx.doi.org/10.20524%2Faog.2016.0082s
  24. Zhou, Y., U. Teomete, O. Dandin, O. Osman, T. Dandinoglu, Bagci, Ulas, and W. Zhao (2016). Computer-Aided Detection (CADx) for Plastic Deformation Fractures in Pediatric Forearm. Computers in Biology and Medicine 78, 120–125.
    https://doi.org/10.1016/j.compbiomed.2016.09.013
  25. Camp, J., U. Bagci, Y. Chu, B. Squier, M. Fraig, S. Uriarte, H. Gu, D. Mollura, & C. Jonsson (2015). Lower Respiratory Tract Infection of the Ferret by 2009 H1N1 Pandemic Influenza A Virus Triggers Biphasic Systemic and Local Neutrophil Recruitment. Journal of Virology.
    https://doi.org/10.1128/JVI.00817-15
  26. Wang et al. (2015). Evaluation of Candidate Vaccine Approaches for MERS-CoV. Nature Communications.
    https://doi.org/10.1038/ncomms8712
  27. Mansoor*, A., U. Bagci, B. Foster, Z. Xu, G. Papadakis, L. Folio, J. Udupa, and D. Mollura (2015). Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends. Radiographics 35(4).
    https://doi.org/10.1148/rg.2015140232
  28. Papadakis*, G. Z., C. Millo, U. Bagci, N. J. Patronas, and C. A. Stratakis (2015). Talc Pleurodesis with intense 18F-FDG activity but no 68Ga-DOTATATE activity on PET/CT. Clinical Nuclear Medicine.
    https://doi.org/10.1097/RLU.0000000000000807
  29. Papadakis*, G. Z., U. Bagci, S. M. Sadowski, N. J. Patronas, and C. A. Stratakis (2015). Ectopic ACTH and CRH co-secreting tumor localized by 68Ga-DOTATATE PET/CT. Clinical Nuclear Medicine.
    https://doi.org/10.1097/RLU.0000000000000806
  30. Papadakis, G., C. Millo, U. Bagci, S. Sadowski, and C. Stratakis (2015). Schmorl Nodes Can Cause Increased 68Ga DOTATATE Activity on PET/CT, Mimicking Metastasis in Patients With Neuroendocrine Malignancy. Clinical Nuclear Medicine.
    https://doi.org/10.1097/RLU.0000000000001065
  31. Xu*, Z., U. Bagci, B. Foster, A. Mansoor, J. K. Udupa, and D. Mollura (2015). A hybrid method for airway segmentation and estimation of its wall boundary surfaces at CT. Medical Image Analysis 24(1), 1–17.
    https://dx.doi.org/10.1016%2Fj.media.2015.05.003
  32. Xu*, Z., U. Bagci, A. Mansoor, G. Kramer-Marek, B. Luna, A. Kubler, B. Dey, B. Foster, G. Z. Papadakis, J. Camp, C. Jonsson, W. Bishai, S. Jain, J. Udupa, and D. Mollura (2015). Computer-Aided Pulmonary Image Analysis of Infectious Lung Diseases in Small Animal Models. Medical Physics 42(7), 3896–3910.
    https://doi.org/10.1118/1.4921618
  33. A.Mansoor, U. Bagci, Z. Xu, B. Foster, J. Elinoff, K. Olivier, A. Suffredini, J. K. Udupa, and D. Mollura (2014). A Generic Approach for Pathological Lung Segmentation. IEEE Transactions on Medical Imaging 33 (12), 2293–2310.
    https://doi.org/10.1109/TMI.2014.2337057
  34. Elinoff, J. M. et al. (2014). Recombinant Human Factor VIIa as Adjunctive Therapy for Alveolar Hemorrhage Following Allogeneic Hematopoietic Stem Cell Transplantation. Biology of Blood and Marrow Transplantation 20(7), 969–978.
    https://dx.doi.org/10.1016/j.bbmt.2014.03.015
  35. Foster, B., U. Bagci, Z. Xu, B. Dey, B. Luna, W. R. Bishai, S. Jain, and D. J. Mollura (2014). Segmentation of PET Images for Computer Aided Functional Quantification of Tuberculosis in Small Animal Models. IEEE Transactions on Biomedical Engineering 61(3), 711–724.
    https://doi.org/10.1109/TBME.2013.2288258
  36. Foster, B., U. Bagci, Z. Xu, A. Mansoor, and D. J. Mollura (2014). A Review on Image Segmentation Methods for Positron Emission Tomography. Computers in Biology and Medicine 50, 76–96.
    https://dx.doi.org/10.1016/j.compbiomed.2014.04.014
  37. Kubler, A., B. Luna, C. Larsson, N. C. Ammerman, B. B. Andrade, M. Orandle, K. W. Bock, Z. Xu, Bagci, Ulas, D. J. Molura, et al. (2014). Mycobacterium tuberculosis dysregulates MMP/TIMP balance to drive rapid cavitation and unrestrained bacterial proliferation. The Journal of Pathology 253(3), 431–444.
    https://doi.org/10.1002/path.4432
  38. Luna, B., A. Kubler, C. Larsson, B. Foster, U. Bagci, D. Mollura, S. Jain, and W. Bishai (2014). In vivo Prediction of Tuberculosis Cavity Formation in Rabbits. Journal of Infectious Diseases 11(3), 481–485.
    https://doi.org/10.1093/infdis/jiu449
  39. Lim*, P., U. Bagci, and L. Bai (2013). Introducing Willmore Flow into Level Set Segmentation of Spinal Vertebrae. IEEE Transactions on Biomedical Engineering 60(1), 115–122.
    https://doi.org/10.1109/TBME.2012.2225833
  40. Bagci, B. Foster, K. Miller-Jaster, B. Luna, B. Dey, W. R. Bishai, C. B. Jonsson, S. Jain, and D. J. Mollura (2013). A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging. European Journal of Nuclear Medicine and Molecular Imaging Research 3(55), 1–20.
    https://doi.org/10.1186/2191-219X-3-55
  41. Bagci, G. Kramer-Marek, and D. Mollura (2013). Automated computer quantification of breast cancer in small-animal models using PET-guided MR image co-segmentation. European Journal of Nuclear Medicine and Molecular Imaging Research 3(49), 1–13.
    https://doi.org/10.1186/2191-219X-3-49
  42. Bagci, K. Miller-Jaster, J.Yao, and D. Mollura (2013). Synergistic combination of clinical and imaging features predicts abnormal imaging patterns of pulmonary infections. Computers in Biology and Medicine 43(9), 1241–1251.
    https://doi.org/10.1016/j.compbiomed.2013.06.008
  43. Bagci, J. K. Udupa, N. Mendhiratta, B. Foster, Z. Xu, J. Yao, X. Chen, and D. Mollura (2013). Joint Segmentation of Anatomical and Functional Images: Applications in Quantification of Lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT Images. Medical Image Analysis 17(8), 929– 945.
    https://doi.org/10.1016/j.media.2013.05.004
  44. Bagci, J. Yao, K. Miller, X. Chen, and D. Mollura (2013). Predicting Future Morphological Changes of Lesions from Radiotracer Uptake in 18F-FDG-PET Images: Longitudinal Assessment of Diseases through Automated Volume Delineations and Texture Correlations. PlosOne 8(2), e57105.
    https://doi.org/10.1371/journal.pone.0057105
  45. Xu*, Z., U. Bagci, A. Kubler, B. Luna, S. Jain, W. Bishai, and D. Mollura (2013). Computer-aided detection and quantification of cavitary tuberculosis from CT scans. Medical Physics 40(11), 113701, 1–14.
    https://doi.org/10.1118/1.4824979
  46. Chen, X., R. Summers, M. Cho, U. Bagci, and J. Yao (2012). An Automatic Method for Renal Cortex Segmentation on CT images: Evaluation on Kidney Donors. Academic Radiology 19(5), 562– 570.
    https://doi.org/10.1016/j.acra.2012.01.005
  47. Chen, X., J. K. Udupa, U. Bagci, Y. Zhuge, and J. Yao (2012). Medical Image Segmentation by Combining Graph Cut and Oriented Active Appearance Models. IEEE Transactions on Image Processing 21(4), 2035–2046.
    https://doi.org/10.1109/TIP.2012.2186306
  48. Kramer-Marek, G., M. Bernardo, D. O. Kiesewetter, U. Bagci, M. Kuban, A. Omer, R. Zielinski, J. Seidel, P. Choyke, and J. Capala (2012). PET Imaging of HER2+ Pulmonary Metastases with 18F-ZHER2:342-Affibody in a Mouse Model; Comparison with 18F-Fluorodeoxyglucose (18F-FDG). Journal of Nuclear Medicine 53(6), 939–946.
    https://doi.org/10.2967/jnumed.111.100354
  49. Bagci, M. Bray, J. Caban, J. Yao, and D. Mollura (2012). Computer-Assisted Detection of Respiratory Tract Infections: A Review. Computerized Medical Imaging and Graphics 36(1), 72–84.
    https://doi.org/10.1016/j.compmedimag.2011.06.002
  50. Bagci, X. Chen, and J. K. Udupa (2012). Hierarchical Scale-Based Multi-Object Recognition of 3D Anatomical Structures. IEEE Transactions on Medical Imaging 31(3), 777–789.
    https://doi.org/10.1109/TMI.2011.2180920
  51. Bagci, J. Yao, A. Wu, J. Caban, A. Suffredini, T. Palmore, O. Aras, and D. J. Mollura (2012). Detection and Quantification of Tree-in-Bud (TIB) Opacities from CT Scans. IEEE Transactions on Biomedical Engineering 59(6), 1620–1632.
    https://dx.doi.org/10.1109/TBME.2012.2190984
  52. Chen, X. and U. Bagci (2011). 3D Automatic Anatomy Segmentation Based on Iterative Graph-Cut-ASM. Medical Physics 38(8), 4610–4622.
    https://doi.org/10.1118/1.3602070
  53. Bagci and L. Bai (2010). Automatic Best Reference Slice (BRS) Selection for Smooth Volume Reconstruction of a Mouse Brain from Histological Sections. IEEE Transactions on Medical Imaging 29(9), 1688–1696.
    https://doi.org/10.1109/TMI.2010.2050594
  54. Bagci, J. K. Udupa, and L. Bai (2010). The Role of Intensity Standardization in Medical Image Registration. Pattern Recognition Letters 31(4), 315–323.
    https://doi.org/10.1016/j.patrec.2009.09.010
  55. Bagci and D. Mamurekli (2009). Determination of Onset of Failure of Rocks in Multiple Failure State Triaxial Tests Using Scale-Based Differential Geometry. Arch. Min. Sci. 54(1), 55–78. 
    https://www.infona.pl/resource/bwmeta1.element.baztech-article-BPZ2-0042-0027
  56. U. Bagci and E. Erzin (2007). Automatic Classification of Musical Genres Using Inter-Genre Similarity. IEEE Signal Processing Letters 8(14), 521–524.
    https://doi.org/10.1109/LSP.2006.8913201. 
  57. Khosravan, N., and Bagci, U. (2018). S4ND: Single-Shot Single-Scale Lung Nodule Detection. MICCAI 2018. [Fox TV and several other media highlights]
    https://doi.org/10.1007/978-3-030-00934-2_88
  58. Mortazi, A., and Bagci, U. (2018). Automatically Designing CNN Architectures for Medical Image Segmentation. Machine Learning in Medical Imaging (MLMI), MICCAI 2018.
    https://doi.org/10.1007/978-3-030-00919-9_12
  59. LaLonde, R., and Bagci, U. (2018). Capsules for Object Segmentation. Medical Imaging with Deep Learning. (MIDL), 2018 [ORAL].
    DOI: 10.1016/j.media.2020.101889
  60. Khosravan, N., Richey, W., and Bagci, U. (2018). Tuberculosis? IEEE EMBC 2018 [Spotlight Oral].
  61. Khosravan, N., and Bagci, U. (2018). Semi-supervised Multi-Task Learning for Lung Cancer Diagnosis. IEEE EMBC 2018 [ORAL].
    https://doi.org/10.1109/EMBC.2018.8512294
  62. Chuquicusma, MJM, Hussein, S., Burt, J., and Bagci, U (2018). How to Fool Radiologists with Generative Adversarial Networks (GANs)? A Visual Turing Test for Lung Cancer Diagnosis. IEEE ISBI 2018.
    https://doi.org/10.1109/ISBI.2018.8363564
  63. Hussein, S., Kandel, P., Corral, JE., Bolan, CW, Wallace, MB, and Bagci, U (2018). Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMNs) with Canonical Correlation Analysis. IEEE ISBI 2018.
    https://doi.org/10.1109/ISBI.2018.8363693
  64. Deghani, H., Laha, S., Kulkarni, P., Biswas, P., Bagci, U., Song, S (2018). “Air-Slicer” for Immersive Visualization of Medical Images. Proceedings of the 2018 Design of Medical Devices Conference (DMD 2018), April 9-12, Minneapolis, MN, 2018.
    https://doi.org/10.1115/DMD2018-6870
  65. Morley, D., Foroosh, H., Shaikh, S., and Bagci, U (2017). Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning. MICCAI-RETOUCH Challenge, 2017.
    https://doi.org/10.48550/arXiv.1708.05464
  66. Mortazi, A., Burt, J., Bagci, U (2017). Multi-planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT. MICCAI STACOM / MM-WHS Challenge 2017.
    https://doi.org/10.1007/978-3-319-75541-0_21
  67. Hussein, S., Q. Cao, Q. Song, and U. Bagci (2017). Risk Stratification of Lung Nodules Using 3D CNN Multi-Task Learning. Information Processing in Medical Imaging (IPMI), 249–259.
    https://doi.org/10.1007/978-3-319-59050-9_20
  68. Hussein, S., R. Gillies, K. Cao, Q. Song, and Bagci, U (2017). TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process. In: IEEE International Symposium on Biomedical Imaging-2017. IEEE.
    https://doi.org/10.1109/ISBI.2017.7950686
  69. Mortazi, A., R. Karim, K. Rhode, J. Burt, and U. Bagci (2017). CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI using Multi-View CNN. In: MICCAI 2017.
    https://doi.org/10.1007/978-3-319-66185-8_43
  70. RaviPrakash, H., M. Korostenskaja, E. Castillo, K. Lee, J. Baumgartner, and Bagci, Ulas (2017). Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning. In: IEEE SMC. [Best conference and student paper nominee]
    https://doi.org/10.1109/SMC.2017.8122658
  71. Torosdagli, N., D. K. Liberton, P. Verma, M. S. J. Lee, S. Pattanaik, and Bagci, Ulas (2017). Robust and fully automated segmentation of mandible from CT scans. In: IEEE ISBI 2017. [Oral, Travel Grant Awardee]
    https://doi.org/10.1109/ISBI.2017.7950734
  72. Buty, M., Z. Xu, M. Gao, Bagci, Ulas, A. Wu, and D. J. Mollura (2016). Characterization of Lung Nodule Malignancy Using Hybrid Shape and Appearance Features. In: International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI. Springer International Publishing, pp.662–670.
    https://doi.org/10.1007/978-3-319-46720-7_77
  73. Khosravan, N., H. Celik, B. Turkbey, R. Cheng, E. McCreedy, M. McAuliffe, S. Bednarova, E. Jones, Chen, and Ulas Bagci (2016). Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation. MICCAI-Medical Computer Vision.
    https://doi.org/10.1007/978-3-319-61188-4_9
  74. Gao, M., U. Bagci, L. Lu, A. Wu, M. Buty, H.-C. Shin, H. Roth, G. Papadakis, A. Depeursinge, R. Summers, Z. Xu, and D. Mollura (2015). Holistic Classification of CT Attenuation Patterns for Interstitial Lung Diseases via Deep Convolutional Neural Networks. In: Deep Learning in Medical Image Analysis, MICCAI 2015.
    https://doi.org/10.1080/21681163.2015.1124249
  75. Hussein, S. and U. Bagci (2015). Transferability of 3D CNN features for Organ Detection. In: NIPS 2015 Workshop on Machine Learning in Healthcare. [Travel Grant Awardee] 
  76. Xu*, Z., U. Bagci, J. Udupa, and D. Mollura (2015). Fuzzy Connectedness Image Co-Segmentation for Hybrid PET/MRI and PET/CT Scans. In: Lecture Notes in Computer Vision and Biomechanics, In: Workshop of Computational Methods for Molecular Imaging-MICCAI 2014.
    https://doi.org/10.1007/978-3-319-18431-9_2
  77. Z.Xu*, U. Bagci, M.Gao, and D. Mollura (2015). Highly Precise Partial Volume Correction for PET Images: An Iterative Approach via Shape Consistency. In: IEEE 10th International Symposium on Biomedical Imaging (ISBI).
    https://doi.org/10.1109/ISBI.2015.7164087
  78. Foster*, B., U. Bagci, and D. Mollura (2014). QAV-PET: A Free Software for Quantitative Analysis and Visualization of PET Images. In: IEEE EMBC 2014.
    https://doi.org/10.1109/EMBC.2014.6943984
  79. Mansoor*, A., U. Bagci, B. Foster, Z. Xu, J. Solomon, D. Douglas, J. Udupa, and D. Mollura (2014). CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans. In: IEEE EMBC 2014.
    https://dx.doi.org/10.1109/EMBC.2014.6943783
  80. Mansoor*, A., U. Bagci, and D. Mollura (2014). Near-optimal KeyPoint Sampling for Fast Pathological Lung Segmentation. In: IEEE EMBC 2014.
    https://doi.org/10.1109/EMBC.2014.6945004
  81. Mansoor*, A., U. Bagci, and D. Mollura (2014). Optimally Stabilized PET Image Denoising Using Trilateral Filtering. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014.
    https://doi.org/10.1007/978-3-319-10404-1_17
  82. Xu*, Z., U. Bagci, and D. Mollura (2014). Accurate and Efficient Separation of Left and Right Lungs from 3D CT Scans: a Generic Hysterisis Approach. In: IEEE EMBC 2014.
    https://doi.org/10.1109/EMBC.2014.6945005
  83. Xu*, Z., U. Bagci, and D. Mollura (2014). Efficient Ribcage Segmentation from CT Scans Using Shape Features. In: IEEE EMBC 2014.
    https://doi.org/10.1109/EMBC.2014.6944229
  84. Xu*, Z., U. Bagci, J. Seidel, D. Homasson, J. Solomon, and D. Mollura (2014). Segmentation Based Denoising of PET Images: An Iterative Approach via Regional Means and Affinity Propagation. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014.
    https://doi.org/10.1007/978-3-319-10404-1_87
  85. Foster, B., U. Bagci, B. Luna, B. Dey, W. Bishai, S. Jain, Z. Xu, and D. J. Mollura (2013). Robust Segmentation and Accurate Target Definition for Positron Emission Tomography Images Using Affinity Propagation. In: Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, pp.1461–1464.
    https://doi.org/10.1109/ISBI.2013.6556810
  86. Bagci and D. J. Mollura (2013). Denoising PET Images Using Singular Value Thresholding and Stein’s Unbiased Risk Estimate. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. Ed. by K. Mori, I. Sakuma, Y. Sato, C. Barillot, and N. Navab. Vol. 8151. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp.115–122.
    https://doi.org/10.1007/978-3-642-40760-4_15
  87. Xu, Z., U. Bagci, B. Foster, A. Mansoor, and D. J. Mollura (2013). Spatially Constrained Random Walk Approach for Accurate Estimation of Airway Wall Surfaces. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. Ed. by K. Mori, I. Sakuma, Y. Sato, C. Barillot, and N. Navab. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp.559–566.
    https://doi.org/10.1007/978-3-642-40763-5_69
  88. Xu*, Z., U. Bagci, B. Foster, and D. J. Mollura (2013). A Hybrid Multi-Scale Approach to Automatic Airway Tree Segmentation from CT Scans. In: Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, pp.1308–1311.
    https://doi.org/10.1109/ISBI.2013.6556772
  89. Caban, J., U. Bagci, A. Mehari, S. Alam, J. R. Fontana, G. J. Kato, and D. J. Mollura (2012). Characterizing NonLinear Dependencies Among Pairs of Clinical Variables and Imaging Data. In: IEEE EMBC 2012, pp.2700–2703.
    https://dx.doi.org/10.1109%2FEMBC.2012.6346521
  90. Lim*, P. H., U. Bagci, O. Aras, Y. Wang, and B. Li (2012). A Novel Spinal Vertebrae Segmentation Framework Combining Geometric Flow and Shape Prior with Level Set. In: IEEE International Symposium in Biomedical Imaging, ISBI-2012, pp.1703–1706.
    https://doi.org/10.1109/ISBI.2012.6235907
  91. Bagci, K. Miller-Jaster, J. Yao, A. Wu, O. Aras, and D. J. Mollura (2012). Automatic Quantification of Tree-In-Bud Patterns from CT Scans. In: IEEE International Symposium in Biomedical Imaging, ISBI-2012, pp.1459–1462.
    https://doi.org/10.1109/ISBI.2012.6235846
  92. Bagci, J. K. Udupa, J. Yao, and D. J. Mollura (2012). Co-segmentation of Functional and Anatomical Images. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Ed. by N. Ayache, H. Delingette, P. Golland, and K. Mori. Vol. 7512. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp.459–467.
    https://doi.org/10.1007/978-3-642-33454-2_57
  93. Caban, J., J. Yao, U. Bagci, and D. J. Mollura (2011). Monitoring Pulmonary Fibrosis by Fusing Clinical, Physiological, and Computed Tomography Features. In: 33rd Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBC 11), pp.6216–6219.
    https://doi.org/10.1109/iembs.2011.6091535
  94. Lim*, P. H., U. Bagci, O. Aras, and B. Li (2011). Identification of Spinal Vertebrae Using Mathematical Morphology and Level Set Method. In: Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE, pp.3105–3107.
    https://doi.org/10.1109/NSSMIC.2011.6152563
  95. Lim*, P. H., U. Bagci, and B. Li (2011). A New Prior Shape Model for Level Set Segmentation. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Ed. by C. San Martin and S.-W. Kim. Vol. 7042. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp.125–132.
    https://doi.org/10.1007/978-3-642-25085-9_14
  96. Bagci, J. K. Udupa, and X. Chen (2011). Intensity Non-Standardness Affects Computer Recognition of Anatomical Structures. In: Proc. of SPIE Medical Imaging 2011. Vol. 7964, pp.79642M.
    https://doi.org/10.1117/12.877779
  97. Bagci, J. K. Udupa, and X. Chen (2011). Orientation Estimation of Anatomical Structures in Medical Images for Object Recognition. In: Proc. of SPIE Medical Imaging 2011. Vol. 7962, pp.79622L.
    https://doi.org/10.1117/12.878184
  98. Bagci, J. Yao, J. Caban, B. E. Turkbey, O. Aras, and D. J. Mollura (2011). A Graph-Theoretic Approach for Segmentation of PET Images. In: 33rd Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBC 11), pp.8479–8482.
    https://doi.org/10.1109/IEMBS.2011.6092092
  99. Bagci, J. Yao, J. Caban, B. E. Turkbey, O. Aras, and D. J. Mollura (2011). Automatic Detection of Tree-in-Bud Patterns for Computer Assisted Diagnosis of Respiratory Tract Infections. In: 33rd Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBC 11), pp.5096–5099.
    https://dx.doi.org/10.1109/IEMBS.2011.6091262
  100. Bagci, J. Yao, J. Caban, A. F. Suffredini, T. N. Palmore, and D. J. Mollura (2011). Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities for CAD Systems. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. Ed. by G. Fichtinger, A. Martel, and T. Peters. Vol. 6893. Lecture Notes in Computer Science. Heidelberg: Springer, pp.215–222.
    https://doi.org/10.1007/978-3-642-23626-6_27
  101. Chen, X., J. K. Udupa, and U. Bagci (2010). 3D Automatic Anatomy Segmentation Based on Iterative-Graph-Cut Active Shape Model. In: Proc. of SPIE Medical Imaging 2010. Vol. 7625, pp.76251T.
    https://doi.org/10.1118/1.3602070
  102. Chen, X., J. Yao, Y. Zhuge, and U. Bagci (2010). 3D Automatic Anatomy Segmentation Based on Graph Cut – Oriented Active Appearance Models. In: IEEE International Conference on Image Processing (ICIP), pp.3653–3656.
    https://doi.org/10.1109/ICIP.2010.5652101
  103. Bagci, J. K. Udupa, and L. Bai (2010). Influences of Standardization on Medical Image Registration. In: Proc. of SPIE Medical Imaging 2010. Vol. 7625, pp.76251X.
    https://doi.org/10.1117/12.843969
  104. Bagci, J. K. Udupa, and X. Chen (2010). Ball-Scale Based Multi-Object Recognition in a Hierarchical Framework. In: Proc. of SPIE Medical Imaging 2010. Vol. 7623, pp.762345.
    https://doi.org/10.1117/12.839920
  105. Bagci and L. Bai (2008). Fully Automatic 3D Reconstruction of Histological Images. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp.991–994.
    https://doi.org/10.1109/ISBI.2008.4541165
  106. Bagci and L. Bai (2008). Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face Recognition. In: IEEE International Conference on Image Processing (ICIP), pp.1640–1643.
    https://doi.org/10.1109/ICIP.2008.4712086
  107. Bagci and L. Bai (2008). Registration of histological images in feature space. In: Proc. of SPIE Medical Imaging 2008. Vol. 6914, pp.69142V.
    https://doi.org/10.1117/12.770219
  108. Bagci and B. Li (2008). Doku Imgelerinin Tam Otomatik Geri Catilmasi. In: Proc. of IEEE Conf. on Processing and Communications Applications (SIU’08), pp.1–4.
    https://doi.org/10.1109/SIU.2008.4632565U. Bagci and B. Li (2008). Medikal Imgelerin Standart Yeginlik Olceginde, Esnek ve Cok Cozunurluklu Cakistirilmasi. In: Proc. of IEEE Conf. on Processing and Communications Applications (SIU’08), pp.1–4.
    https://doi.org/10.1109/SIU.2008.4632564
  109. Bagci and L. Bai (2007). A Comparison of Daubechies and Gabor Wavelets for Classification of MR Images. In: IEEE International Conference on Signal Processing and Communications (ICSPC), pp.676–679.
    https://doi.org/10.1109/ICSPC.2007.4728409
  110. Bagci and L. Bai (2007). Multi-resolution Elastic Medical Image Registration in Standard Intensity Scale. In: IEEE Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI), pp.305–312.
    https://doi.org/10.1109/SIU.2008.4632564
  111. Bagci and B. Li (2007). Detecting Alzheimer Disease in Magnetic Resonance Brain Images Using Gabor Wavelets. In: Proc. of IEEE Conf. on Processing and Com. Applications (SIU’07), pp.1–4.
    https://doi.org/10.1109/SIU.2007.4298553
  112. Bagci and E. Erzin (2006). Inter Genre Similarity Modelling for Automatic Music Genre Classification. In: Digital Audio Effects-DAFx-2006, pp.153–156.
    DOI:10.1109/SIU.2006.1659788
  113. Bagci and E. Erzin (2006). Muzik Turlerinin Siniflandirilmasinda Benzer Kesisim Bilgileri Uygulamalari. In: Proc. of IEEE Conf. on Processing and Communications Applications (SIU’06), pp.1–4.
    https://doi.org/10.1109/SIU.2006.1659788
  114. Bagci and E. Erzin (2006). Muzik Turlerinin siniflandirilmasinda Siniflandiricilarin Yukseltilmesi. In: Proc. of IEEE Conf. on Processing and Communications Applications (SIU’06), pp.1– 3. 
  115. U. Bagci and E. Erzin (2005). Boosting Classifiers for Music Genre Classification. In: International Symposium on Computer and Information Sciences (ISCIS). Vol. 33. Lecture Notes in Computer Sci-ence, pp.575–584.
    https://doi.org/10.1007/11569596_60
  116. Deep Learning in Radiology and Its Future Trends. RSNA 2018.
  117. Shaikh, S., Morley, D., Foroosh, H., and Bagci, Ulas (2018). Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning. In: 51th Annual Retina Society Meeting.
  118. Corral, JE., Hussein, S., Kandel, P., Bolan, CW., Bagci, U., Wallace, MB (2018). Deep Learning to Diagnose Intraductal Papillary Mucinous Neoplasm (IPMN) with MRI. Digestive Disease Week 2018.
  119. Mortazi, A., Burt, J., and Bagci, U (2017). Deep Learning for Cardiac MRI: Automatically Segmenting Left Atrium Expert Human Level Performance. Radiological Society of North America (RSNA), 103rd Scientific Assembly and Annual Meeting, November 26- December 1, 2017 McCormick Place, Chicago. [Oral]
  120. Hussein, S., Mortazi, A., RaviPrakash, H., Burt, J., and Bagci, U (2017). Deep Learning Applications in Radiology, Recent Developments, Challenges, and Potential Solutions. Radiological Society of North America (RSNA), 103rd Scientific Assembly and Annual Meeting, November 26- December 1, 2017 McCormick Place, Chicago. [Certificate of Merit Awardee]
  121. Celik, H., B. Turkbey, P. Choyke, R. Cheng, E. McCreedy, M. McAuliffe, N. Khosravan, Ulas Bagci, and B. Wood (2017). Eye Tracking System for Prostate Cancer Diagnosis Using Multi-Parametric MRI. In: ISMRM.
  122. Mortazi, A., Bagci, Ulas, and J. Burt (2017). Machine Learning for Cardiac MRI: Automated Mapping of Left Atrium and Pulmonary Veins with Human Level Performance. In: 45th Annual Meeting of North American Society for Cardiovascular Imaging. [Young Investigator Award Nominee]
  123. Shaikh, S., T. Ozerderm, A. Carmack, B. Thiel, Bagci, Ulas (2017). Motion Stabilization in Retinal Video Angiography Using Serial Rigid Registration. In: 50th Annual Retina Society Meeting.
  124. Papadakis, G., C. Millo, Bagci, U, N. Patronas, and M. Collins (2016). Value of F-18-NaF PET/CT imaging in the assessment of Gorham-Stout disease activity. In: European Journal of Nuclear Medicine and Molecular Imaging. Vol. 43. SPRINGER 233 SPRING ST, NEW YORK, NY 10013 USA, pp.S597–S597. [Oral]
  125. Green, A., U. Bagci, P. V. Kelly, and M. Osman (2015). Brown adipose tissue detected by FDG PET/CT is associated with less central obesity compared to body mass index matched controls. In: SNMMI (Society of Nuclear Medicine and Molecular Imaging).
  126. Green, A., U. Bagci, P. V. Kelly, and M. Osman (2015). Brown Adipose Tissue Detected by FDG PET/CT is Associated with Less Visceral Fat. In: SNMMI (Society of Nuclear Medicine and Molecular Imaging).
  127. Papadakis, G., A. Karageorgiadis, U. Bagci, R. Casas, C. Millo, N. Patronas, and C. Stratakis (2015). Value of 18F-FDG-PET/CT in Localizing Ectopic ACTH/CRH Co-secreting Tumors, Causing Cushing Syndrome (CS), in Children and Adolescents. In: Radiological Society of North America (RSNA), 101st Scientific Assembly and Annual Meeting, November 29 – December 4, 2015, McCormick Place, Chicago. [Oral]
  128. Souly, N., G. Papadakis, U. Teomete, and U. Bagci (2015). A New Saliency Metric for Precise Denoising PET Images for Better Visualization and Accurate Segmentation. In: Radiological Society of North America (RSNA), 101st Scientific Assembly and Annual Meeting, November 29 – December 4, 2015, McCormick Place, Chicago. [Oral]
  129. Teomete, U., G. Papadakis, O. Osman, O. Dandin, and U. Bagci (2015). From Signal to Screen: The Science Behind Radiologic Images. In: Radiological Society of North America (RSNA), 101st Scientific Assembly and Annual Meeting, November 29 – December 4, 2015, McCormick Place, Chicago.
  130. Teomete, U., Y. Zhou, O. Dandin, W. Zhao, T. Dandinoglu, O. Osman, and U. Bagci (2015). Plastic Bowing Fractures of the Pediatric Forearm: Evaluation of a Novel Computer Aided Method for Detection. In: Radiological Society of North America (RSNA), 101st Scientific Assembly and Annual Meeting, November 29 – December 4, 2015, McCormick Place, Chicago. [Oral]
  131. U. Bagci, G. Papadakis, Z. Xu, A. Green, M. Osman, and M. Shah (2015). Nuclear Medicine Meets Computer Vision: Increasing Role of Computerized Detection, Tracking, Diagnosis, and Quantification of PET/CT and PET/MRI Studies. In: SNMMI (Society of Nuclear Medicine and Molecular Imaging).
  132. Xu, Z., M. Gao, U. Bagci, and D. Mollura (2015). Recent Advances in Techniques for PET Image Denoising and Partial Volume Correction. In: Radiological Society of North America (RSNA), 101st Scientific Assembly and Annual Meeting, November 29 – December 4, 2015, McCormick Place, Chicago. 
  133. Xu*, Z., U. Bagci, M. Gao, and M. Shah (2015). Improved PET image quantification via iterative denoising and partial volume correction. In: SNMMI (Society of Nuclear Medicine and Molecular Imaging).
  134. A.Mansoor*, U. Bagci, and D. Mollura (2014). Noise Adaptive Multi-resolution technique to accurately denoise PET, MRI-PET, and PET-CT images. In: J Nucl Med. 2014; 55 (Supplement 1):2050.
  135. Camp, J. V., U. Bagci, M. Fraigb, H. Guoc, S. M. Uriarte, D. J. Mollura, and C. B. Jonsson (2014). Multifocal neutrophil infiltration and inflammation in lungs of ferrets infected with 2009 H1N1 Influenza A virus clinical isolate. In: Annual Meeting of the American Society for Virology.
  136. Johnson, R. F., D. J. Mollura, L. E. Via, U. Bagci, N. Oberlande, C. J. Bartos, J. Solomon, J. Johnson, R. Holbrook, D. Thomasson, G. G. Olinger, L. E. Hensley, and P. B. Jahrling (2014). Evaluation of MERS CoV Induced Disease in Two Species of Nonhuman Primate the Common Marmoset and Rhesus Monkey by Computed Tomography. In: 13th International Nidovirus Symposium.
  137. Johnson, R. F., U. Bagci, D. J. Mollura, C. J. Bartos, N. Oberlander, M. R. Holbrook, D. Thomasson, G. Olinger, P. B. Jahrling, and L. E. Hensley (2014). Evaluation of MERS CoV Induced Disease in the Rhesus Macaque by Computed Tomography. In: ASM Biodefence.
  138. Mansoor, A., U. Bagci, B. Foster, Z. Xu, G. Papadakis, J. Udupa, and D. Mollura (2014). Computerized detection and classification of pulmonary pathologies from CT images: current approaches, challenges, and future trends. In: Radiological Society of North America (RSNA).
  139. Mansoor, A., U. Bagci, Z. Xu, B. Foster, G. Papadakis, and D. Mollura (2014). Lung Lobe Volume-try as a Reliable Biomarker: Methods for Automatic Extraction of Lobes from CT Scans, and Fissure Integrity Scoring. In: Radiological Society of North America (RSNA).
  140. Ollinger, G. G., R. F. Johnson, U. Bagci, L. Via, J. Solomon, D. Hammoud, D. J. Mollura, R. C. Reba, N. Oberlander, C. Bartos, D. Douglas, K. Cooper, M. R. Holbrook, L. E. Hensley, and P. B. Jahrling (2014). Use of Imaging for development of animal models of Biosafety Level (BSL) 3 and 4 agents. In: World Molecular Imaging Congress.
  141. Papadakis, G., U. Bagci, B. Foster, Z. Xu, A. Mansoor, N. Patronas, C. Stratakis, and D. Mollura (2014). Automated Computer-derived SUV and Metabolic Tumor Volume Measurements of Biopsy Proven Lesions: Comparison with Radiologist-derived PET-CT Imaging. In: Radiological Society of North America (RSNA).
  142. Papadakis, G., U. Bagci, Z.Xu, C. S. K.A. Kissell, and D. Mollura (2014). Detection and Quantification of Brown Fat Tissue using PET-CT Scans: A Novel Computer Aided Detection System. In: Annual Congress of the European Association of Nuclear Medicine-EANM.
  143. Spergel, A. R., C. Chen, C. Beegle, P. Littel, M. Garofalo, S. Anaya-O’Brien, M. Marquesen, U. Bagci, D. Mollura, J. Gallin, and H. Malech (2014). The use of radiolabelled 18-F-2-deoxy- 2-fluro-glucose (18F-FDG) in combined positron emission tomography-computed tomography (PET-CT) to evaluate infection: lessons learned from a case series of 23 patients with Chronic Granulomatous Disease (CGD). In: American Academy of Allergy, Asthma & Immunology Annual Meeting.
  144. U. Bagci, Z.Xu, and D. Mollura (2014). Recent Advances in PET, PET-CT, and MRI-PET Image Segmentation Techniques. In: J Nucl Med. 2014; 55 (Supplement 1):1280.
  145. Xu, Z., U. Bagci, A. Mansoor, B. Foster, G. Papadakis, J. J. Udupa, and D. Mollura (2014). The State-of-the-art and Recent Advances in Pulmonary Image Analysis Techniques. In: Radiological Society of North America (RSNA).
  146. Z.Xu*, U. Bagci, and D. Mollura (2014). Diffusion based enhancement of PET images for improved diagnostic measurements in clinical nuclear medicine. In: J Nucl Med. 2014; 55 (Supplement 1):2051.
  147. Foster*, B., U. Bagci, A. Mansoor, Z. Xu, and D. Mollura (2013). Challenges, Techniques, and Advancements for State-of-the-Art PET Image Segmentation. In: Radiological Society of North America (RSNA), 99th Scientific Assembly and Annual Meeting, Dec 1-6, 2013, McCormick Place, Chicago.
  148. Foster*, B., U. Bagci, Z. Xu, B. Dey, B. Luna, W. Bishai, S. Jain, and D. Mollura (2013). Affinity Propagation Clustering Determines Distributed Uptake Regions in PET Images: A Computer-Aided Approach for Quantification of Pulmonary Infections in Small Animals. In: J Nucl Med. 2013; 54 (Supplement 2):313. [Oral]
  149. Foster*, B., U. Bagci, Z. Xu, A. Mansoor, B. Luna, B. Dey, W. BIshai, C. Jonson, S. Jain, and D. Mollura (2013). Quantitative Analysis of Infectious Lung Disease from Serial PET-CT Scans in Small Animal Models. In: Radiological Society of North America (RSNA), 99th Scientific Assembly and Annual Meeting, Dec 1-6, 2013, McCormick Place, Chicago. 
  150. Foster*, B., U. Bagci, X. Zu, A. Mansoor, B. Dey, B. Luna, W. Bishai, S. Jain, and D. Mollura (2013). A Method for Segmenting Multi-Focal Radiotracer Uptake in PET Images to Quantify Tuberculosis in Rabbits. In: Radiological Society of North America (RSNA), 99th Scientific Assembly and Annual Meeting, Dec 1-6, 2013, McCormick Place, Chicago. [Oral]
  151. Mansoor*, A., U. Bagci, B. Foster, Z. Xu, and D. Mollura (2013). How to Correctly Denoise PET and MRI-PET Images: Current Approaches, Constraints, and Future Trends. In: Radiological Society of North America (RSNA), 99th Scientific Assembly and Annual Meeting, Dec 1-6, 2013, McCormick Place, Chicago.
  152. Mansoor*, A., U. Bagci, B. Foster, Z. Xu, J. Udupa, and D. Mollura (2013). A Robust Pathological Lung Segmentation Platform Using Fuzzy-Connectedness with Patient-specific Modeling. In: Radiological Society of North America (RSNA), 99th Scientific Assembly and Annual Meeting, Dec 1-6, 2013, McCormick Place, Chicago.
  153. Mansoor*, A., U. Bagci, B. Foster, Z. Xu, J. Udupa, and D. Mollura (2013). Pathological Lung Segmentation in Computed Tomography (CT) Images. In: Radiological Society of North America (RSNA), 99th Scientific Assembly and Annual Meeting, Dec 1-6, 2013, McCormick Place, Chicago.
  154. Sandouk*, A., U. Bagci, Z. Xu, A. Mansoor, B. Foster, and D. Mollura (2013). Accurate Quantification of Brown Adipose Tissue through PET-guided CT Image Segmentation. In: J Nucl Med. 2013; 54 (Supplement 2):318.
  155. U. Bagci, B. Foster, Z. Xu, B. Luna, B. Dey, W. Bishai, C. Jonsson, S. Jain, and D. Mollura (2013). A Computational Platform for Quantification of Infectious Lung Disease Using PET-CT Imaging. In: J Nucl Med. 2013; 54 (Supplement 2):314.
  156. Xu*, Z., U. Bagci, J. Udupa, and D. Mollura (2013). Simultaneous Segmentation from Hybrid MRI-PET and PET-CT Images Using Fuzzy Connectedness Image Co-segmentation. In: Radiolog-ical Society of North America (RSNA), 99th Scientific Assembly and Annual Meeting, Dec 1-6, 2013, McCormick Place, Chicago.
  157. Luna, B. B., K. Miller-Jaster, B. Foster, U. Bagci, D. Mollura, S. Jain, and W. Bishai (2012). Qualitative and Quantitative Analysis of Inflammation in Pulmonary Tuberculosis in Rabbit using F18-FDG-PET/CT Imaging: A multi-Parametric Approach. In: Molecular Imaging of Infectious Diseases: Current Status and Future Challenges.
  158. N.Mendhiratta*, Z. Xu*, B. Foster*, U. Bagci, and D. Mollura (2012). Accurate and Robust Quantification of Hybrid MRI-PET and PET-CT Images through a Novel Joint-Segmentation Method. In: Molecular Imaging of Infectious Diseases: Current Status and Future Challenges BEST POSTER PRIZE.
  159. U. Bagci, O. Aras, and D. Mollura (2012). Correlation of Anatomical and Functional Information from PET-CT Images. In: J Nucl Med. 2012; 53 (Supplement 1):2266.
  160. U. Bagci, J. Udupa, K. Jaster-Miller, and D. Mollura (2012). Automatic Segmentation Methods for Abnormal Activities from PET, PET-CT, and MRI-PET Images. In: RSNA.
  161. U. Bagci, J. Udupa, K. Jaster-Miller, and D. Mollura (2012). Simultaneous Segmentation of Abnormal Activities from Hybrid MRI-PET. In: RSNA Highlighted in AuntMinnie.
  162. Caban, J., J. Yao, U. Bagci, and D. J. Mollura (2011). Computer-based Quantitative Modeling of Chest CT findings in Pulmonary Hypertension and its Association with Physiologic and Clinical Variables. In: Radiological Society of North America (RSNA), 97th Scientific Assembly and Annual Meeting, Nov 27-Dec 2, 2011, McCormick Place, Chicago. 
  163. Caban, J., J. Yao, U. Bagci, and D. J. Mollura (2011). Quantitative Measurements of Chest CT Using Texture Analysis (RSNA MERIT AWARD). In: Radiological Society of North America (RSNA), 97th Scientific Assembly and Annual Meeting, Nov 27-Dec 2, 2011, McCormick Place, Chicago. 
  164. U. Bagci, X. Chen, L. Bai, D. Mollura, B. Turkbey, and O. Aras (2011). Registration, Reconstruction, and Analysis of Serial Histological Sections. In: Radiological Society of North America (RSNA), 97th Scientific Assembly and Annual Meeting, Nov 27-Dec 2, 2011, McCormick Place, Chicago. 
  165. U. Bagci, X. Chen, J. K. Udupa, L. Bai, D. J. Mollura, B. Turkbey, and O. Aras (2011). Model Based Segmentation Methods: Multi-Organ Segmentation Platform. In: Radiological Society of North America (RSNA), 97th Scientific Assembly and Annual Meeting, Nov 27-Dec 2, 2011, McCormick Place, Chicago. 
  166. U. Bagci, X. Chen, J. K. Udupa, B. Li, S. Messian, B. Turkbey, and O. Aras (2011). Quantitative Assessment of Multiple Sclerosis (MS) Lesions in Longitudinal MRI Studies. In: Radiological Society of North America (RSNA), 97th Scientific Assembly and Annual Meeting, Nov 27-Dec 2, 2011, McCormick Place, Chicago. 
  167. U. Bagci, X. Chen, J. Udupa, S. Histed, S. Perez-Pujol, B. Turkbey, and O. Aras (2011). Automated Analysis of Multi-detector CT images for Preoperative Assessment of Living Renal Donors. In: Radiological Society of North America (RSNA), 97th Scientific Assembly and Annual Meeting, Nov 27-Dec 2, 2011, McCormick Place, Chicago. 
  168. U. Bagci, B. Turkbey, S. Perez-Pujol, D. Mollura, and O. Aras (2011). Is There a Reliable Correlation Between computer-aided diagnosis (CAD) Results from CT Images and Information from PET Images in Longitudinal Studies: An Example Study in Interstitial Lung Disease. In: Radiological Society of North America (RSNA), 97th Scientific Assembly and Annual Meeting, Nov 27-Dec 2, 2011, McCormick Place, Chicago. 
  169. U. Bagci and X.Chen (2011). Characteristics of Shape Functional in Iterative Graph Cut Active Shape Model Segmentation (IGCASM). Tech. rep. NIH, Technical Report. 
  170. U. Bagci, J. Yao, J. Caban, T. N. Palmore, A. F. Suffredini, and D. J. Mollura (2011). CAD for Pulmonary Infections: Automatic Detection of Tree-in-Bud Opacities. In: Radiological Society of North America (RSNA), 97th Scientific Assembly and Annual Meeting, Nov 27-Dec 2, 2011, McCormick Place, Chicago. 
  171. U. Bagci, J. Yao, J. Caban, T. Palmore, A. Suffredini, A. Wu, and D. Mollura (2011). Quantification of Small Airway Pulmonary Infections: Subjective Visual Grading versus Objective Quantification Through a CAD System. In: Radiological Society of North America (RSNA), 97th Scientific Assembly and Annual Meeting, Nov 27-Dec 2, 2011, McCormick Place, Chicago. 
  172. U. Bagci (2010). “Automatic Anatomy Recognition and Registration”. PhD thesis. University of Nottingham. 
  173. U. Bagci and J. K. Udupa (2008). The Role of Standardization in Medical Image Registration. Tech. rep. MIPG Technical Report-341. 
  174. U. Bagci and J. K. Udupa (2008). Towards Efficient Medical Image Registration Methods. In: Marie Curie Workshop-Barcelona.
  175. U. Bagci (2007). Fundamental Issues of Registration: Applications for change analysis in health and disease. Tech. rep. CMIAG Technical Report. 
  176. U. Bagci (2005). Boosting Classifiers for Automatic Music Genre Classification. Tech. rep. MSc Thesis, Koc University

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Bagci Lab 2023, Department of Radiology, Feinberg School of Medicine, Northwestern University.