________________________
PUBLICATIONS
Relational Reasoning Network for Anatomical Landmarking, Journal of Medical Imaging, 2023
A multi-centre polyp detection and segmentation dataset for generalisability assessment, Nature Data, 2023
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation, ISBI, 2023 (link to be published)
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification, IEEE ICECCME 2023 (link to be published)
Deep Learning Prediction of MRI-Induced Power Absorption in Patients with Deep Brain Stimulation Leads, IEEE EMBC 2023 (link to be published)
AI EMPOWERED AUTOMATIC VOLUME DELINEATION OF LIVER FROM CT SCANS FOR DIAGNOSTIC WORKFLOW, DDW 2023 (link to be published)
A FULLY AUTOMATIC AI SYSTEM FOR PANCREAS SEGMENTATION FROM MULTICENTER MRI SCANS, DDW 2023 (link to be published)
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation, MMM 2023 (link to be published)
AI for the detection of COVID19 pneumonia on chest CT using multinational datasets. Nature Communications, 2020.
Instance-Level Microtubule Tracking. IEEE Transactions on Medical Imaging, 2020.
Deep Geodesic Learning for Segmentation and Anatomical Landmarking. IEEE Transactions on Medical Imaging, 2019.
RETOUCH-The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge. IEEE Transactions on Medical Imaging, 2019.
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches. IEEE Transactions on Medical Imaging, 2019.
Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans. IEEE Transactions on Medical Imaging, 2017.
A Generic Approach for Pathological Lung Segmentation. IEEE Transactions on Medical Imaging, 2015.
Hierarchical Scale-Based Multi-Object Recognition of 3D Anatomical Structures. IEEE Transactions on Medical Imaging, 2012.
Automatic Best Reference Slice (BRS) Selection for Smooth Volume Reconstruction of a Mouse Brain from Histological Sections. IEEE Transactions on Medical Imaging, 2010.
Capsules for Biomedical Image Segmentation. Medical Image Analysis, 2020.
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye Tracking, Sparse Attentional Model, and Deep Learning. Medical Image Analysis, 2019.
Joint Solution for PET Image Segmentation, Denoising, and Partial Volume Correction. Medical Image Analysis, 2018.
CorteXpert: A model-based method for automated renal cortex segmentation. Medical Image Analysis, 2017.
A hybrid method for airway segmentation and estimation of its wall boundary surfaces at CT. Medical Image Analysis, 2015.
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, 2013.
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.
Segmentation of PET Images for Computer Aided Functional Quantification of Tuberculosis in Small Animal Models. IEEE Transactions on Biomedical Engineering, 2014.
Introducing Willmore Flow into Level Set Segmentation of Spinal Vertebrae. IEEE Transactions on Biomedical Engineering, 2013.
Detection and Quantification of Tree-in-Bud (TIB) Opacities from CT Scans. IEEE Transactions on Biomedical Engineering, 2012.
Computer-Aided Pulmonary Image Analysis of Infectious Lung Diseases in Small Animal Models. Medical Physics, 2012.
Computer-aided detection and quantification of cavitary tuberculosis from CT scans. Medical Physics, 2013.
3D Automatic Anatomy Segmentation Based on Iterative Graph-Cut-ASM. Medical Physics, 2011.
Medical Image Segmentation by Combining Graph Cut and Oriented Active Appearance Models. IEEE Transactions on Image Processing, 2012.
Artificial Intelligence Assisted Infrastructure Assessment Using Mixed Reality Systems. Journal of the Transportation Research, 2019.
Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks. Journal of Digital Imaging, 2019.
Single-channel Sparse Non-Negative Blind Source Separation Method for Automatic 3D Delineation of Lung Tumor in PET Images. IEEE Journal of Biomedical and Health Informatics, 2016.
Evaluation of Candidate Vaccine Approaches for MERS-CoV. Nature Communications, 2015.
PUBLICATIONS
1. 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), AI in Clinical Medicine: A Practical Guide for Healthcare Professionals
2. Anwar, SM, and Bagci, U (2019). Artificial Intelligence as Another Set of Eyes in Breast Cancer Diagnosis. Journal of Medical Artificial Intelligence.
http://dx.doi.org/10.21037/jmai.2019.04.03
3. Korostenskaja, M., RaviPrakash, H., Bagci, U., Lee, KH., Chen, PC., Kapeller, C., Salinas, C., Westerfield, M., Ralescu, A., Xiang, J., Baumgartner, J., Elsayed, M., and Castillo, E (2019). Gold Standard for Epilepsy/Tumor Surgery Coupled with Deep Learning Offers Independence to a Promising Functional Mapping Modality. Brain-Computer Interface Research, A State-of-the-Art Summary 7, Springer.
https://doi.org/10.1007/978-3-030-05668-1_2
4. RaviPrakash, R., Watane, A., Jambawalikar, S., and Bagci, U (2019). Deep Learning for Functional Brain Connectivity: Are We There Yet? Springer (Deep Learning and CNN for Medical Image Computing, Invited).
https://doi.org/10.1007/978-3-030-13969-8_17
5. Hussein, S., and Bagci, U (2019). Lung Imaging and CADx. Chapter 14: How Deep Learning is Changing the Landscape of Lung Cancer Diagnosis. CRC Press (invited).
https://doi.org/10.1201/9780429055959
6. 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
7. 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
8. U. Bagci (2010). Automatic Anatomy Recognition and Registration-Medical Image Processing and Analysis. LAP LAMBERT Academic Publishing.
ISBN-10: 3838380142
1. Neslisah Torosdagli, Syed Anwar, Payal Verma, Denise K. Liberton, Janice S. Lee, Wade W. Han, Ulas Bagci, “Relational reasoning network for anatomical landmarking,” J. Med. Imag. 10(2), 024002 (2023), doi: 10.1117/1.JMI.10.2.024002.
1. Rahimi, A., Hosntalab, M., Babapour, F., Amoui, M., Bagci, U (2022). An automatic segmentation framework for computer-assisted renal scintigraphy. Medical & Biological Engineering & Computing, 1-11. DOI: 10.1007/s11517-022-02717-7
2. Keles, E., Irmakci, I., Bagci, U (2022). Musculoskeletal MR Image Segmentation with Artificial Intelligence. Advances in Clinical Radiology, 4(1), 179-188.
3. Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2022). Federated Learning for Medical Applications: A Taxonomy, Current Trends, and Research Challenges. arXiv preprint arXiv:2208.03392.
4. Lalonde, R., Khosravan, N., & Bagci, U. (2022). Deformable capsules for object detection. arXiv preprint arXiv:2104.05031.
5. Aytekin, Idil, Onat Dalmaz, Kaan Gonc, Haydar Ankishan, Emine U. Saritas, Ulas Bagci, Haydar Celik, and Tolga Cukur. (2022). COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers. arXiv preprint arXiv:2207.09529. IEEE JBHI.
6. Çomoglu, S., Öztürk, S., Topçu, A., Kulali, F., Kant, A., Sobay, R., … & Yilmaz, G. (2021). The Role of CO-RADS Scoring System in the Diagnosis of COVID-19 Infection and its Correlation with Clinical Signs. DOI: 10.2174/1573405617666210827150937
7. Vu, J., Nguyen, B. T., Bhusal, B., Baraboo, J., Rosenow, J., Bagci, U., … & Golestanirad, L. (2021). Machine Learning-Based Prediction of MRI-Induced Power Absorption in the Tissue in Patients With Simplified Deep Brain Stimulation Lead Models. IEEE Transactions on Electromagnetic Compatibility, 63(5), 1757-1766.
DOI: 10.1109/temc.2021.3106872
8. Karaaslan, E., Bagci, U., & Catbas, N. (2021). A novel decision support system for long-term management of bridge networks. Applied Sciences, 11(13), 5928.
9. Marvasti, A. E., Marvasti, E. E., Bagci, U., & Foroosh, H. (2021). Maximum probability theorem: A framework for probabilistic machine learning. IEEE Transactions on Artificial Intelligence, 2(3), 214-227.
10. Karaaslan, E., Bagci, U., & Catbas, F. N. (2021). Attention-guided analysis of infrastructure damage with semi-supervised deep learning. Automation in Construction, 125, 103634.
11. Masoudi, S., Mehralivand, S., Harmon, S.A., Lay, N., Lindenberg, L., Mena, E., Pinto, P.A., Citrin, D.E., Gulley, J.L., Wood, B.J. and Dahut, W.L., 2021. Deep learning based staging of bone lesions from computed tomography scans. IEEE Access, 9, pp.87531-87542.
12. RaviPrakash, H., Anwar, S. M., Biassou, N. M., & Bagci, U. (2021). Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players. Frontiers in Neuroscience, 15, 629478.
13. Desai, A. D., Caliva, F., Iriondo, C., Mortazi, A., Jambawalikar, S., Bagci, U., … & IWOAI Segmentation Challenge Writing Group. (2021). The international workshop on osteoarthritis imaging knee MRI segmentation challenge: a multi-institute evaluation and analysis framework on a standardized dataset. Radiology: Artificial Intelligence, 3(3), e200078.
14. He, B., Song, Y., Wang, L., Wang, T., She, Y., Hou, L., Zhang, L., Wu, C., Babu, B.A., Bagci, U. and Waseem, T., 2021. A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics. Translational Lung Cancer Research, 10(2), p.955.
15. LaLonde, R., Xu, Z., Irmakci, I., Jain, S., & Bagci, U. (2021). Capsules for biomedical image segmentation. Medical image analysis, 68, 101889.
16. Masoudi, Samira, Stephanie AA Harmon, Sherif Mehralivand, Stephanie M. Walker, Harish Raviprakash, Ulas Bagci, Peter L. Choyke, and Baris Turkbey. “Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research.” Journal of Medical Imaging 8, no. 1 (2021): 010901.
17. Anwar, Syed Muhammad, Ismail Irmakci, Drew A. Torigian, Sachin Jambawalikar, Georgios Z. Papadakis, Can Akgun, Jutta Ellermann, Mehmet Akcakaya, and Ulas Bagci. “Semi-supervised deep learning for multi-tissue segmentation from multi-contrast MRI.” Journal of Signal Processing Systems (2020): 1-14.
18. Stember, Joseph N., Haydar Celik, David Gutman, Nathaniel Swinburne, Robert Young, Sarah Eskreis-Winkler, Andrei Holodny et al. “Integrating eye tracking and speech recognition accurately annotates MR brain images for deep learning: proof of principle.” Radiology: Artificial Intelligence 3, no. 1 (2020): e200047.
19. Cyrus, Elena, Rachel Clarke, Dexter Hadley, Zoran Bursac, Mary Jo Trepka, Jessy G. Dévieux, Ulas Bagci et al. “The impact of COVID-19 on African American communities in the United States.” Health Equity 4, no. 1 (2020): 476-483.
20. Parasa, S., Wallace, M., Bagci, U., Antonino, M., Berzin, T., Byrne, M., Celik, H., Farahani, K., Golding, M., Gross, S. and Jamali, V., 2020. Proceedings from the first global artificial intelligence in gastroenterology and endoscopy Summit. Gastrointestinal endoscopy, 92(4), pp.938-945.
21. Saeed, Sanay Muhammad Umar, Syed Muhammad Anwar, Humaira Khalid, Muhammad Majid, and Ulas Bagci. “EEG based classification of long-term stress using psychological labeling.” Sensors 20, no. 7 (2020): 1886.
22. Harmon, S., et al (2020). Artificial Intelligence for the Detection of COVID-19 Pneumonia on Chest CT Using Multinational Dataset. Nature Communications.
https://doi.org/10.1038/s41467-020-17971-2
23. Pankaj Kulkarni, Sakura Sikander, Pradipta Biswas, Sumit Laha, Heather Cornnell, Jeremy R Burt, Ulas Bagci, Sang-Eun Song (2020). Development of a Device-to-Image Registration Free Needle Guide for Magnetic Resonance Imaging-Guided Targeted Prostate Biopsy. Journal of Medical Devices 14(4).
https://doi.org/10.1115/1.4047874
24. 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 (2020). PET/CT and PET/MRI in Optalhalmic Oncology. International Journal of Oncology.
https://doi.org/10.3892/ijo.2020.4955
25. Tirosh, A., RaviPrakash, H., Papadakis, GZ., Tatsi, C., Belyavskaya, E., Chahralampos, L., Lodish, MB., Bagci, U., Stratakis, CA (2019). Computerized Analysis of Brain MR parameters dynamics in young patients with Cushing-Syndrome – a case control study. The Journal of Clinical Endocrinology and Metabolism.
https://doi.org/10.1210/clinem/dgz303
26. Hoogenboom, S., Bagci, U., Wallace, MB (2020). AI in Gastroenterology. Current State of Play and Potential. How will it affect our practice and when? Techniques in Gastrointestinal Endoscopy, 150634.
https://doi.org/10.1016/j.tgie.2019.150634
27. Saha, S., LaLonde R., Carmack, A., Foroosh, H., Olson, J.C., Shaikh, S., Bagci, U (2020). Analysis of Video Retinal Angiography with Deep Learning and Eulerian Magnification. Frontiers in Computer Science.
28. Syed, A., Irmakci, I., Torigian, D., Jambawalikar, S., Akgun, C., Akcakaya, M., Bagci, U (2020). Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI. Journal of Signal Processing Systems.
29. RaviPrakash, H., Korostenskaja, M., Castillo, E., Lee, KH., Salinas, CM., Baumgartner, J., Scampinatoe, C., Bagci, U (2020). Deep Learning Provides Exceptional Accuracy to ECoG-based Functional Language Mapping for Epilepsy Surgery. Frontiers in Neuroscience.
https://doi.org/10.3389/fnins.2020.00409
30. Saeed, SMU., Anwar, S., Khalid, H., Majid, M., Bagci, U (2020). Electroencephalography based Classification of Long-Term Stress Using Psychological Labeling. Sensors, 20(7), 1886.
https://doi.org/10.3390/s20071886
31. N. Torosdagli, Anwar, S., D. Liberton, P. Verma, J. Lee, H. Wan, Bagci, U (2020). Relational Reasoning Network for Anatomical Landmarking. SPIE Medical Imaging (accepted-minor revision).
32. Masoodi, S., Razi, A., Wright, C., Gatlin, J., Bagci, U (2020). Instance-level Microtubule Tracking. IEEE Transactions on Medical Imaging.
https://doi.org/10.1109/TMI.2019.2963865
33. Bugunovic, H., Hrvoje Bogunovi´c, Freerk Venhuizen, Sophie Klimscha, Stefanos Apostolopoulos, Alireza Bab-Hadiashar, Ulas Bagci, Mirza Faisal Beg, Loza Bekalo, Qiang Chen, Carlos Ciller, Karthik Gopinath, Amirali K. Gostar, Kiwan Jeon, Zexuan Ji, Sung Ho Kang, Dara D. Koozekanani, Donghuan Lu, Dustin Morley, Keshab K. Parhi, Hyoung Suk Park, Abdolreza Rashno, Marinko Sarunic, Saad Shaikh, Jayanthi Sivaswamy, Ruwan Tennakoon, Shivin Yadav, Sandro De Zanet, Sebastian M. Waldstein, Bianca S. Gerendas, Caroline Klaver, Clara I. Sánchez, Ursula Schmidt-Erfurth (2019). RETOUCH-The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge. IEEE Transactions on Medical Imaging, 2019.
https://doi.org/10.1109/TMI.2019.2901398
34. Vinsard, DG., Mori, Y., Misawa, M., Kudo, S., Rastogi, A., Bagci, U., Rex, DK., Wallace, MB (2019). Quality Assurance of Computer-Aided Detection and Diagnosis in Colonoscopy. Gastrointestinal Endoscopy, 2019.
https://doi.org/10.1016/j.gie.2019.03.019
35. J. Hurtado, S. Hussein, P. Kandel, C. Bolan, U. Bagci, and M. Wallace (2019). Deep Learning to Classify Intraductal Papillary Mucinous Neoplasms Using Magnetic Resonance Imaging. Pancreas, 2019.
https://doi.org/10.1097/MPA.0000000000001327
36. Papadakis, GZ., Manikis, GC, Karantanas, AH, Florenzano, P., Bagci, U., Marias, K., Collins, MT, Alison M. Boyce (2019). 18F-NaF PET/CT imaging in fibrous dysplasia of bone: implications for evaluation of disease activity and treatment. Journal of Bone and Mineral Research, 2019.
https://doi.org/10.1002/jbmr.3738
37. Stember, JN., Celik, H., Krupinski, E., Chang, P., Mutasa, S., Wood, B., Lignelli, A., Moonis, G., Jambawalikar, S., Bagci, U (2019). Eye-Tracking for Deep Learning Segmentation Using Convolutional Neural Networks: a proof-of-principle application to meningiomas. Journal of Digital Imaging, 2019.
https://doi.org/10.1007/s10278-019-00220-4
38. Karaaslan, E., Bagci, U., and Catbas, F.N. (2019). Artificial Intelligence Assisted Infrastructure Assessment Using Mixed Reality Systems. Journal of Transportation Research.
https://doi.org/10.1177%2F0361198119839988
39. N. Torosdagli, D. Liberton, P. Verma, M. Sincan, J. Lee, Bagci, U (2019). Deep Geodesic Learning for Segmentation and Anatomical Landmarking. IEEE Transactions on Medical Imaging.
https://doi.org/10.1109/TMI.2018.2875814
40. 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
41. Khosravan, N, Celik, H, Turkbey, B., Jones, E, Wood, B., Bagci, U (2019). A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye Tracking, Sparse Attentional Model, and Deep Learning. Medical Image Analysis.
https://doi.org/10.1016/j.media.2018.10.010
42. Hussein, S., MM, Kandel, P, Bolan, CW, Wallace, MB, Bagci, U (2019). Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches. IEEE Transactions on Medical Imaging.
https://doi.org/10.1109/TMI.2019.2894349
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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
64. L. Wang et al. (2015). Evaluation of Candidate Vaccine Approaches for MERS-CoV. Nature Communications.
https://doi.org/10.1038/ncomms8712
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. U. 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
79. U. 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
80. U. 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
81. U. 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
82. U. 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
83. 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
84. 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
85. 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
86. 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
87. U. 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
88. U. 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
89. U. 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
90. 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
91. U. 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
92. U. 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
93. U. 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.
94. 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.2006.891320
1.
1. Tomar, NK., Jha, D., Bagci, U (2023). DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation. SPIE 2023.
https://doi.org/10.48550/arXiv.2210.13595
2. Srivastava, A., Jha, D., Keles, E., Aydogan, B., Abazeed, M., & Bagci, U. (2022). An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation. arXiv preprint
https://doi.org/10.48550/arXiv.2208.07417
3. Irmakci, I., Unel, Z. E., Ikizler-Cinbis, N., & Bagci, U. (2022). Multi-Contrast MRI Segmentation Trained on Synthetic Images. arXiv preprint arXiv:2207.02469. IEEE EMBC 2022
4. Proietto Salanitri, F., Bellitto, G., Palazzo, S., Irmakci, I., Wallace, M.B., Bolan, C.W., Engels, M., Hoogenboom, S., Aldinucci, M., Bagci, U., Giordano, D., Spampinato, C. (2022):. “Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images.” arXiv e-prints: arXiv-2206. IEEE EMBC 2022
5. Jha, D., Rauniyar, A., Johansen, H. D., Johansen, D., Riegler, M. A., Halvorsen, P., & Bagci, U. (2022, June). Video Analytics in Elite Soccer: A Distributed Computing Perspective. In 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM) (pp. 221-225). IEEE SAM.
DOI: 10.1109/SAM53842.2022.9827827
6. Tomar, N. K., Shergill, A., Rieders, B., Bagci, U., & Jha, D. (2022). TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation. arXiv e-prints, arXiv-2206. IEEE MM
https://doi.org/10.48550/arXiv.2206.08985
7. Srivastava, A., Tomar, N. K., Bagci, U., & Jha, D. (2022). Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network. arXiv e-prints, arXiv-2206.
https://doi.org/10.48550/arXiv.2206.08298
8. Tomar, N. K., Srivastava, A., Bagci, U., & Jha, D. (2022). Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network. arXiv e-prints, arXiv-2206.
https://doi.org/10.48550/arXiv.2206.06264
9. Zhang, Z., & Bagci, U. (2022). Dynamic Linear Transformer for 3D Biomedical Image Segmentation. arXiv e-prints, arXiv-2206.
https://doi.org/10.48550/arXiv.2206.00771
10. Demir, Ugur, Zheyuan Zhang, Bin Wang, Matthew Antalek, Elif Keles, Debesh Jha, Amir Borhani, Daniela Ladner, and Ulas Bagci (2022). “Transformer based Generative Adversarial Network for Liver Segmentation.” arXiv e-prints: arXiv-2205.
https://doi.org/10.48550/arXiv.2205.10663
11. Tomar, N. K., Jha, D., Bagci, U., & Ali, S. (2022). TGANet: Text-guided attention for improved polyp segmentation. arXiv preprint arXiv:2205.04280. MICCAI 2022.
https://doi.org/10.48550/arXiv.2205.04280
12. Aytekin, I., Dalmaz, O., Ankishan, H., Saritas, E. U., Bagci, U., Cukur, T., & Celik, H. (2022, April). Detecting COVID-19 from respiratory sound recordings with transformers. In Medical Imaging 2022: Computer-Aided Diagnosis (Vol. 12033, pp. 25-32). SPIE. https://doi.org/10.48550/arXiv.2205.04280
13. Marvasti, A. E., Marvasti, E. E., & Bagci, U. (2022). Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem. arXiv preprint arXiv:2203.12145. https://doi.org/10.48550/arXiv.2203.12145
14. Isler, I., Lisle, C., Rineer, J., Kelly, P., Turgut, D., Ricci, J., & Bagci, U. (2022, April). Enhancing organ at risk segmentation with improved deep neural networks. In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 814-820). SPIE.
https://doi.org/10.48550/arXiv.2202.01866
15. Zheng, C., Chen, X., Nguyen, B. T., Sanpitak, P., Vu, J., Bagci, U., & Golestanirad, L. (2021, November). Predicting RF Heating of Conductive Leads During Magnetic Resonance Imaging at 1.5 T: A Machine Learning Approach. 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
16. Proietto Salanitri, F., Bellitto, G., Irmakci, I., Palazzo, S., Bagci, U., & Spampinato, C. (2021, September). Hierarchical 3D Feature Learning for Pancreas Segmentation. In International Workshop on Machine Learning in Medical Imaging (pp. 238-247). Springer, Cham.
https://doi.org/10.48550/arXiv.2109.01667
17. Demir, U., Irmakci, I., Keles, E., Topcu, A., Xu, Z., Spampinato, C., … & Bagci, U. (2021, September). Information bottleneck attribution for visual explanations of diagnosis and prognosis. In International Workshop on Machine Learning in Medical Imaging (pp. 396-405). Springer, Cham.
https://doi.org/10.48550/arXiv.2104.02869
18. Masoudi, S., Harmon, S., Mehralivand, S., Lay, N., Bagci, U., Wood, B.J., Pinto, P.A., Choyke, P. and Turkbey, B., 2021, April. No-Reference Image Quality Assessment Of T2-Weighted Magnetic Resonance Images In Prostate Cancer Patients. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 1201-1205). IEEE.
DOI:10.1109/ISBI48211.2021.9434027
19. Pino, Carmelo, Simone Palazzo, Francesca Trenta, Francesca Cordero, Ulas Bagci, Francesco Rundo, Sebastiano Battiato, Daniela Giordano, Marco Aldinucci, and Concetto Spampinato. “Interpretable deep model for predicting gene-addicted non-small-cell lung cancer in ct scans.” In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 891-894. IEEE, 2021.
DOI:10.1109/ISBI48211.2021.9433832
20. Palazzo, Simone, Giovanni Bellitto, Luca Prezzavento, Francesco Rundo, Ulas Bagci, Daniela Giordano, Rosalia Leonardi, and Concetto Spampinato. “Deep Multi-stage Model for Automated Landmarking of Craniomaxillofacial CT Scans.” In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9982-9987. IEEE, 2021.
DOI:10.1109/ICPR48806.2021.9412910
21. Murabito, Francesca, Simone Palazzo, F. Proietto Salanitri, Francesco Rundo, Ulas Bagci, Daniela Giordano, Rosalia Leonardi, and Concetto Spampinato. “Deep recurrent-convolutional model for automated segmentation of craniomaxillofacial CT scans.” In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9062-9067. IEEE, 2021.
DOI:10.1109/ICPR48806.2021.9413084
22. RaviPrakash, H., Anwar, SM., Bousio, N., Bagci, U (2020). Variational Capsule Encoder. ICPR 2020.
https://doi.org/10.48550/arXiv.2010.09102
23. Yousaf, Sobia, Harish RaviPrakash, Syed Muhammad Anwar, Nosheen Sohail, and Ulas Bagci. “State-of-the-art in brain tumor segmentation and current challenges.” 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
24. Nawaz, A., Anwar, S., Liaqat, R., Iqbal, J., Bagci, U (2020). Deep Convolutional Neural Networks Based Classification of Alzheimer’s Disease Using MRI Data. IEEE INMIC 2020.
https://doi.org/10.48550/arXiv.2101.02876
25. LaLonde, R., Torigian, D., Bagci, U (2020). Encoding High-Level Visual Attributes in Capsules for Explainable Medical Diagnoses. MICCAI 2020.
https://doi.org/10.48550/arXiv.1909.05926
26. Shaheen, A., Bagci, U., Mohy-ud-Din, H. (2020). Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features. RNO-AI (Radiomics and Radiogenomics in Neuro-oncology), MICCAI 2020.
https://link.springer.com/chapter/10.1007/978-3-030-66843-3_25
27. Yousaf, S., Anwar, S, RaviPrakash, H., Bagci, U. (2020). Brain Tumor Survival Prediction using Radiomics Features. RNO-AI (Radiomics and Radiogenomics in Neuro-oncology), MICCAI 2020.
https://doi.org/10.48550/arXiv.2009.02903
28. LaLonde, R., Kandel, P., Spampinato, C., Wallace, M, Bagci, U (2020). Diagnosing Colorectal Polyps in the Wild with Capsule Networks. IEEE ISBI 2020.
https://doi.org/10.48550/arXiv.2001.03305
29. Masoudi, S., Anwar, S., Harmon, S., Choyke, P., Turkbey, B., Bagci., U (2020). Adipose Tissue Segmentation in Unlabeled Abdomen MRI Using Cross Modality Domain Adaptation. IEEE EMBC 2020.
DOI: 10.1109/EMBC44109.2020.9176009
30. LaLonde, R., Tanner, I., Nikiforaki, K., Papadakis, GZ, Kandel, P., Bolan, CW, Wallace, M., Bagci, U (2019). INN: Inflated Neural Networks for IPMN Diagnosis. MICCAI 2019.
https://doi.org/10.1007/978-3-030-32254-0_12
31. Khosravan, N., Mortazi, A., Wallace, M., Bagci, U (2019). PAN: Projective Adversarial Network for Medical Image Segmentation. MICCAI 2019.
https://doi.org/10.1007/978-3-030-32226-7_8
32. Mortazi, A., Khosravan, N., Torigian, DA, Kurugol, S., Bagci, U. (2019). Weakly Supervised Segmentation by A Deep Geodesic Prior. MICCAI 2019-MLMI.
https://doi.org/10.1007/978-3-030-32692-0_28
33. Liu, Y., Khosravan, N., Liu, Y., Stember, J., Bagci, U., Jambawalikar, S (2019). Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans. MICCAI 2019-DART.
https://doi.org/10.1007/978-3-030-33391-1_8
34. Anwar, S., Tooba, A., Rafique, K., RaviPrakash, H., Mohy-ud-din, H., Bagci, U (2019). A Survey on Recent Advancements for AI-Enabled Radiomics in Neuro-Oncology. MICCAI 2019-RNO-AI.
https://doi.org/10.1007/978-3-030-40124-5_3
35. Arsalan, A., Majid, M., Anwar, SM, Bagci, U (2019). Classification of Perceived Human Stress Using Psychological Signals. IEEE EMBC.
https://doi.org/10.1109/JBHI.2019.2926407
36. Arsalan, A., Majid, M., Anwar, SM, Bagci, U (2019). Emotion Classification in Response to Tactile Enhanced Multimedia Using Frequency Domain Features of Brain Signals. IEEE EMBC.
https://doi.org/10.1109/EMBC.2019.8857632
37. Irmakci, I., Anwar, S., Torigian, D., Bagci, U (2010). Deep Learning for Musculoskeletal Image Analysis. IEEE ASILOMAR 2019 (invited article).
38. Kulkarni, P., Laha, S., Sikander, S., Cornell, H., Bagci, U., Burt, J., and Song, SS (2019). MRI-guided, Transperineal Prostate Biopsy Using Fixed Coordinate Needle Guide: Initial Feasibility Study. Design of Medical Devices Conference, 2019.
https://doi.org/10.1115/DMD2019-3281
39. 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
40. 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
41. 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
42. Khosravan, N., Richey, W., and Bagci, U. (2018). Tuberculosis? IEEE EMBC 2018 [Spotlight Oral].
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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]
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. U. 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
69. 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
70. 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
71. 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
72. 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
73. U. 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
74. U. 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
75. 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
76. 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
77. 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
78. U. 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
79. U. 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
80. U. 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
81. U. 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
82. U. 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
83. 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
84. 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
85. U. 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
86. U. 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
87. U. 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
88. U. 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
89. U. 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
90. U. 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.2008.4632565
91. U. 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
92. U. 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
93. U. 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
94. U. 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
95. U. 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
96. U. 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
97. U. 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.
98. 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
1. Engels, M. M., Hoogenboom, S., Demir, U., Pattilachan, T., Van Hooft, J. E., Bi, Y., … & Bagci, U. (2022). 541: DEEP LEARNING MODELS IN THE CLASSIFICATION OF INTRADUCTAL PAPILLARY MUCINOUS NEOPLASMS WITH VISUAL EXPLANATION METHODS TO HIGHLIGHT AREAS OF INTEREST FOR ALGORITHM DECISION PROCESS. Gastroenterology, 162(7), S-127.
2. Dam, E. B., Desai, A., Deniz, C., Rajamohan, H., Regatte, R., Iriondo, C., … & Chaudhari, A. (2022). MULTI-INSTITUTIONAL LARGE-SCALE VALIDATION OF 8 METHODS FOR AUTOMATIC KNEE MRI SEGMENTATION FOR USE IN CLINICAL TRIALS. Osteoarthritis and Cartilage, 30, S291-S292.
3. Rodriguez, AC., Sharma, A., Tanner, I., Kandel, P., Lalonde, R., Livingston, D., Manoj, J., Raimondo, M., Wallace, M., Bagci, U (2019). Brown Adipose Tissue Activation Detected by Artificial Intelligence Assisted Radiomics: An Early Biomarker for Pancreatic Ductal Adenocarcinoma (PDAC). Gastroenterology.
4. Hoogenboom, S. A., Ravi, K., Engels, M. M., Irmakci, I., Keles, E., Bolan, C. W., … & Bagci, U. (2021). 79 MISSED DIAGNOSIS OF PANCREATIC DUCTAL ADENOCARCINOMA DETECTION USING DEEP CONVOLUTIONAL NEURAL NETWORK. Gastroenterology, 160(6), S-18.
5. Kandel, P., Lalonde, R., Ciofoaia, V., Wallace, M., Bagci, U (2019). Colorectcal Polyp Diagnosis With Contemporary Artificial Intelligence. Gastrointestinal Endoscopy.
6. Papadakis, GZ., Manikis, GC, Karantanas, AH., Marias, K., Bagci, U., Florenzano, P., Collins, MT, Boyce, AM (2019). F-18-NaF Uptake by Fibrous Dysplasia Bone Lesions is Positively Associated with Bone Turnover Markers (BTMs). EJNMMI 2019.
7. Papadakis, GZ., Manikis, GC, Karantanas, AH., Marias, K., Bagci, U., Florenzano, P., Collins, MT, Boyce, AM (2019). Prognostic Utility of F-18-NaF PET/CT Imaging for Fractures in Patients with Fibrous Dysplasia of Bone. EJNMMI 2019.
8. Papadakis, GZ., Jha, S., Karantanas, AH., Marias, K., Bagci, U., Bhattachharyya, T (2018). Prospective Evaluation of the Application of F-18-NaF PET/CT Imaging in Melorheostosis. EJNMMI 2018.
9. Mortazi, A., Papadakis, GZ., Teomete, U., and Bagci, U. (2018). Cardiac Image Analysis with Deep Learning Methods. RSNA 2018.
10. Hussein, S., Mortazi, A., RaviPrakash, R., Khosravan, N., Papadakis, GZ., Teomete, U., Bagci, U. (2018). Virtual Radiologists: Current Status of Deep Learning in Radiology and Its Future Trends. RSNA 2018.
11. 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.
12. 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.
13. 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]
14. 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]
15. 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.
16. 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]
17. 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.
18. 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]
19. 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).
20. 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).
21. 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]
22. 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]
23. 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.
24. 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]
25. 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).
26. 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.
27. 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).
28. 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.
29. 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.
30. 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.
31. 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.
32. 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).
33. 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).
34. 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.
35. 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).
36. 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.
37. 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.
38. 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.
39. 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).
40. 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.
41. 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.
42. 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]
43. 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.
44. 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]
45. 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.
46. 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.
47. 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.
48. 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.
49. 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.
50. 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.
51. 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.
52. 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.
53. 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.
54. 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.
55. 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.
56. 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.
57. 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.
58. 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.
59. 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.
60. 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.
61. 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.
62. 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.
63. U. Bagci and X.Chen (2011). Characteristics of Shape Functional in Iterative Graph Cut Active Shape Model Segmentation (IGCASM). Tech. rep. NIH, Technical Report.
64. 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.
65. 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.
66. U. Bagci (2010). “Automatic Anatomy Recognition and Registration”. PhD thesis. University of Nottingham.
67. U. Bagci and J. K. Udupa (2008). The Role of Standardization in Medical Image Registration. Tech. rep. MIPG Technical Report-341.
68. U. Bagci and J. K. Udupa (2008). Towards Efficient Medical Image Registration Methods. In: Marie Curie Workshop-Barcelona.
69. U. Bagci (2007). Fundamental Issues of Registration: Applications for change analysis in health and disease. Tech. rep. CMIAG Technical Report.
70. U. Bagci (2005). Boosting Classifiers for Automatic Music Genre Classification. Tech. rep. MSc Thesis, Koc University
1. Rahimi, A., Hosntalab, M., Babapour, F., Amoui, M., Bagci, U (2022). An automatic segmentation framework for computer-assisted renal scintigraphy. Medical & Biological Engineering & Computing, 1-11.
Bagci Lab 2023, Department of Radiology, Feinberg School of Medicine, Northwestern University.
