Dr. Jianfei Liu

National Institutes of Health, USA

Email: [email protected]

Qualifications

2011 Ph.D., University of North Carolina at Charlotte, USA, Computer Science

2004 M.Sc., Institute of Automation, Chinese Academy of Sciences, China, Information Technology

2001 B.Sc., University of Science and Technology Beijing, China, Computer Engineering

Publications (Selected)

1. Kim, B., Mathai, T. S., Helm, K., Mukherjee, P., Liu, J., & Summers, R. M. (2025). Automated Classification of Body MRI Sequences Using Convolutional Neural Networks. Academic Radiology, 32(3), 1192-1203.
2. Liu, L., Liu, J., Santra, B., Parnell, C., Mukherjee, P., Mathai, T., ... & Summers, R. M. (2025). Utilizing domain knowledge to improve the classification of intravenous contrast phase of CT scans. Computerized Medical Imaging and Graphics, 119, 102458.
3. Oluigbo, D., Mathai, T. S., Santra, B., Mukherjee, P., Liu, J., Jha, A., ... & Summers, R. M. (2024). Weakly supervised detection of pheochromocytomas and paragangliomas in CT using noisy data. Computerized Medical Imaging and Graphics, 116, 102419.
4. Hou, B., Mathai, T. S., Liu, J., Parnell, C., & Summers, R. M. (2024). Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study. International journal of computer assisted radiology and surgery, 19(8), 1589-1596.
5. Nguyen, A. M., Mathai, T. S., Liu, L., Liu, J., & Summers, R. M. (2024, May). Automated Measurement of Pericoronary Adipose Tissue Attenuation and Volume in Ct Angiography. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE.
6. Liu, J., Shafaat, O., Bhadra, S., Parnell, C., Harris, A., & Summers, R. M. (2024). Improved subcutaneous edema segmentation on abdominal CT using a generated adipose tissue density prior. International journal of computer assisted radiology and surgery, 19(3), 443-448.
7. Liu, J., Shafaat, O., & Summers, R. M. (2023, October). A dual-branch network with mixed and self-supervision for medical image segmentation: an application to segment edematous adipose tissue. In Workshop on medical image learning with limited and noisy data (pp. 158-167). Cham: Springer Nature Switzerland.
8. Anand, A., Liu, J., Shen, T. C., Linehan, W. M., Pinto, P. A., & Summers, R. M. (2023, April). Automated classification of intravenous contrast enhancement phase of ct scans using residual networks. In Medical Imaging 2023: Computer-Aided Diagnosis (Vol. 12465, pp. 129-134). SPIE.
9. Nag, M. K., Liu, J., Shin, S. Y., Lee, S., Lee, J. M., & Summers, R. M. (2023, March). Body location embedded 3D U-Net (BLE-U-Net) for ovarian cancer ascites segmentation on CT scans. In Proceedings of SPIE--the International Society for Optical Engineering (Vol. 12567, p. 125670E).
10. Yan, H., Liu, S., Zhang, J., Liu, J., & Li, T. (2021). Utilizing pre-determined beam orientation information in dose prediction by 3D fully-connected network for intensity modulated radiotherapy. Quantitative imaging in medicine and surgery, 11(12), 4742.
11. Liu, J., Yan, H., Cheng, H., Liu, J., Sun, P., Wang, B., ... & Luo, S. (2021). CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation. Quantitative imaging in medicine and surgery, 11(12), 4820.
12. Zhang, J., Liu, S., Yan, H., Li, T., Mao, R., & Liu, J. (2020). Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions. Physics in Medicine & Biology, 65(20), 205013.
13. Jiang, D., Yan, H., Chang, N., Li, T., Mao, R., Du, C., ... & Liu, J. (2020). Convolutional neural network‐based dosimetry evaluation of esophageal radiation treatment planning. Medical Physics, 47(10), 4735-4742.
14. Liu, J., Han, Y. J., Liu, T., Aguilera, N., & Tam, J. (2020). Spatially aware dense-linkNet based regression improves fluorescent cell detection in adaptive optics ophthalmic images. IEEE journal of biomedical and health informatics, 24(12), 3520-3528.
15. Liu, J., Han, Y. J., Liu, T., & Tam, J. (2020, February). Spatially aware deep learning improves identification of retinal pigment epithelial cells with heterogeneous fluorescence levels visualized using adaptive optics. In Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 11317, pp. 328-333). SPIE.

Profile Details

https://www.cc.nih.gov/meet-our-doctors/jliu

https://www.researchgate.net/scientific-contributions/Jianfei-Liu-2066514306

https://scholar.google.com/citations?user=IdP9DboAAAAJ&hl=en