Ti Bai

Timeline

Publications

1. 2025 Ti Bai, Xenia Ray, David Parsons, Mu-Han Lin, “Cone Beam Computed Tomography-Guided Online Adaptive Radiation Therapy: Clinical Insights From a Nationwide Staffing Survey,” International Journal of Radiation Oncology Biology Physics. paper
2. 2025 Dan Nguyen, Anjali Balagopal, Ti Bai, Michael Dohopolski, Mu-Han Lin, Steve Jiang, “Prior guided deep difference meta-learner for fast adaptation to stylized segmentation,” Machine Learning: Science and Technology.
3. 2025 Siqiu Wang, Chien-Yi Liao, Byongsu Choi, Sean All, Ti Bai, Justin Visak, Dominic Moon, et al., “Impact of Manual Contour Editing on Plan Quality for Online Adaptive Radiation Therapy for Head and Neck Cancer,” Practical Radiation Oncology.
4. 2025 Hengrui Zhao, Biling Wang, Michael Dohopolski, Ti Bai, Steve Jiang, Dan Nguyen, “Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer,” Machine Learning: Science and Technology.
5. 2025 Chien-Yi Liao, Austen Matthew Maniscalco, Hengrui Zhao, Ti Bai, Byongsu Choi, Dominic Moon, Daniel Yang, et al., “Contour uncertainty assessment for MD-omitted daily adaptive online head and neck radiotherapy,” Radiotherapy and Oncology.
6. 2024 Biling Wang, Michael Dohopolski, Ti Bai, Junjie Wu, Raquibul Hannan, Neil Desai, Aurelie Garant, Dan Nguyen, Xinlei Wang, Mu-Han Lin, Robert Timmerman, Steve Jiang, “Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy,” Machine Learning: Science and Technology.
7. 2024 Anjali Balagopal, Michael Dohopolski, Young Suk Kwon, Steven Montalvo, Howard Morgan, Ti Bai, Dan Nguyen, Xiao Liang, Xinran Zhong, Mu-Han Lin, Neil Desai, Steve Jiang, “Deep Learning-based Automatic Segmentation of the Internal Pudendal Artery for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer,” Physics and Imaging in Radiation Oncology.
8. 2024 Hengrui Zhao, Xiao Liang, Boyu Meng, Michael Dohopolski, Byongsu Choi, Bin Cai, Mu-Han Lin, Ti Bai, Dan Nguyen, Steve Jiang, “Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy,” Physics and Imaging in Radiation Oncology 31:100610.
9. 2023 Xiao Liang, Jaehee Chun, Howard Morgan, Ti Bai, Dan Nguyen, Justin Park, Steve Jiang, “Segmentation by test time optimization for CBCT based adaptive radiation therapy,” Medical Physics 50(4):1947-1961. paper
10. 2023 Xiao Liang, Howard Morgan, Ti Bai, Michael Dohopolski, Dan Nguyen, Steve Jiang, “Deep learning based direct segmentation assisted by deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy,” Physics in Medicine & Biology. paper
11. 2023 Shaojie Chang, Yongfeng Gao, Marc J. Pomeroy, Ti Bai, Hao Zhang, Siming Lu, Perry J. Pickhardt, Amit Gupta, Michael J. Reiter, Elaine S. Gould, Zhengrong Liang, “Exploring dual-energy CT spectral information for machine learning-driven lesion diagnosis in pre-log domain,” IEEE Transactions on Medical Imaging. paper
12. 2023 Xiao Liang, Allen Yen, Ti Bai, Andrew Godley, Chenyang Shen, Junjie Wu, Boyu Meng, et al., “Bony structure enhanced synthetic CT generation using Dixon sequences for pelvis MR only radiotherapy,” Medical Physics 50(12):7368-7382.
13. 2023 Xi Chen, Chaoyang Zhang, Ti Bai, Shaojie Chang, “Improving Spectral CT Image Quality Based on Channel Correlation and Self-Supervised Learning,” IEEE Transactions on Computational Imaging 9:1084-1097. paper
14. 2023 Shaohua Zhi, Yinghui Wang, Haonan Xiao, Ti Bai, Hong Ge, Bing Li, Chenyang Liu, Wen Li, Tian Li, Jing Cai, “Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution,” IEEE Transactions on Medical Imaging. paper
15. 2022 Wufeng Xue, Heng Cao, Junqiang Ma, Ti Bai, Tianfu Wang, Dong Ni, “Improved Segmentation of Echocardiography With Orientation-Congruency of Optical Flow and Motion-Enhanced Segmentation,” IEEE Journal of Biomedical and Health Informatics. paper
16. 2022 Ti Bai, Anjali Balagopal, Michael Dohopolski, Howard E. Morgan, Rafe McBeth, Jun Tan, Mu-Han Lin, David J. Sher, Dan Nguyen, Steve Jiang, “A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Editing,” Radiology: AI. paper
17. 2022 Hua-Chieh Shao, Jing Wang, Ti Bai, Jaehee Chun, Justin C. Park, Steve B. Jiang, You Zhang, “Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling,” Physics in Medicine & Biology. paper
18. 2021 Ti Bai, Biling Wang, Dan Nguyen, Bao Wang, Bin Dong, Wenxiang Cong, Mannudeep K. Kalra, Steve Jiang, “Deep Interactive Denoiser (DID) for X-Ray Computed Tomography,” IEEE Transactions on Medical Imaging. paper
19. 2021 Ti Bai, Biling Wang, Dan Nguyen, Steve Jiang, “Probabilistic Self-learning Framework for Low-dose CT Denoising,” Medical Physics. paper
20. 2021 Ti Bai, Dan Nguyen, Biling Wang, Steve Jiang, “Deep High-Resolution Network for Low Dose X-ray CT Denoising,” Journal of Artificial Intelligence for Medical Sciences. paper
21. 2021 Ti Bai, Biling Wang, Dan Nguyen, Steve Jiang, “Deep Dose Plugin: Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm,” Machine Learning: Science and Technology. paper
22. 2021 Jianhui Ma, Dan Nguyen, Ti Bai, Michael Folkerts, Xun Jia, Weiguo Lu, Linghong Zhou, Steve Jiang, “A Feasibility Study on Deep Learning Based Individualized 3D Dose Distribution Prediction,” Medical Physics. paper
23. 2021 Wen Li, Ti Bai, Samaneh Kazemifar, Dan Nguyen, Yaochung Weng, Yafen Li, Jun Xia, Jing Xiong, Yaoqin Xie, Amir Owrangi, Steve Jiang, “Synthesizing CT Images from MR Images with Deep Learning: Model Generalization for Different Datasets through Transfer Learning,” Biomedical Physics & Engineering Express. paper
24. 2017 Ti Bai, Hao Yan, Xun Jia, Steve B. Jiang, Ge Wang, Xuanqin Mou, “Z-Index Parameterization (ZIP) for Volumetric CT Image Reconstruction via 3D Dictionary Learning,” IEEE Transactions on Medical Imaging. paper
25. 2017 Ti Bai, Hao Yan, Luo Ouyang, David Staub, Jing Wang, Xun Jia, Steve B. Jiang, Xuanqin Mou, “Data correlation based noise level estimation for cone beam projection data,” Journal of X-ray Science and Technology. paper
26. 2015 Xu Yuan, Bai Ti, Yan Hao, Ouyang Luo, Pompos A, Wang Jing, Zhou Linghong, Jiang Steve, Jia Xun, “A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy,” Physics in Medicine and Biology 60(9):3567. paper
27. 2014 H. Yan, X. Wang, F. Shi, Ti Bai, M. Folkerts, L. Cervino, S. B. Jiang, X. Jia, “Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy: Cone/ring artifact correction and multiple GPU implementation,” Medical Physics 41. paper

Code

• Welcome to MAIA Lab - PyTorch tutorial.
• CodeBase - PyTorch-based training framework.
• iRT AutoSegmentation - automatic 3D medical image segmentation.
• Auto3DSeg-monai - command generation for model training based on MONAI Auto3DSeg.