Publications

Ghimire, K., Chen, Q., Feng, X. (2022). Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear Model. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2021. Lecture Notes in Computer Science, 13209. Springer, Cham. https://doi.org/10.1007/978-3-030-98253-9_13

Duan, J., Bernard, M., Downes, L., Willows, B., Feng, X., Mourad, W. F., St Clair, W., Chen, Q. (2022). Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process. Medical physics, 49(4), 2570–2581. https://doi.org/10.1002/mp.15525

Castle, J. R., Duan, J., Feng, X., Chen, Q. (2022). Development of a virtual source model for Monte Carlo‐based independent dose calculation for varian linac. Journal of Applied Clinical Medical Physics, e13556. https://doi.org/10.1002/acm2.13556

Feng, X., Chen, Q. (2021). Organ-Specific Segmentation Versus Multi-Class Segmentation Using U-Net. Auto-Segmentation for Radiation Oncology, CRC Press 125-132. https://www.taylorfrancis.com/chapters/edit/10.1201/9780429323782-11/

Ghimire, K., Chen, Q., Feng, X. (2021). Patch-Based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions. In: Andrearczyk, V., Oreiller, V., Depeursinge, A. (eds) Head and Neck Tumor Segmentation. HECKTOR 2020. Lecture Notes in Computer Science, 12603. Springer, Cham. https://doi.org/10.1007/978-3-030-67194-5_9

Chen, W., Li, Y., Dyer, B.A. et al. Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images. Radiat Oncol 15, 176 (2020). https://doi.org/10.1186/s13014-020-01617-0

Feng, X., Tustison, N. J., Patel, S. H., Meyer, C. H. (2020). Brain tumor segmentation using an ensemble of 3d u-nets and overall survival prediction using radiomic features. Frontiers in computational neuroscience, 14, 25. https://doi.org/10.3389/fncom.2020.00025

Feng, X., Bernard, M. E., Hunter, T., Chen, Q. (2020). Improving accuracy and robustness of deep convolutional neural network based thoracic OAR segmentation. Physics in Medicine & Biology, 65(7), 07NT01. https://doi.org/10.1088/1361-6560/ab7877

Feng, X., Qing, K., Tustison, N. J., Meyer, C. H., Chen, Q. (2019). Deep convolutional neural network for segmentation of thoracic organs‐at‐risk using cropped 3D images. Medical physics, 46(5), 2169-2180. https://doi.org/10.1002/mp.13466

Yang, J., Veeraraghavan, H., Armato III, S. G., Farahani, K., Kirby, J. S., Kalpathy‐Kramer, J., et al. (2018). Autosegmentation for thoracic radiation treatment planning: a grand challenge at AAPM 2017. Medical physics, 45(10), 4568-4581. https://doi.org/10.1002/mp.13141

Handsfield, L. L., Jones, R., Wilson, D. D., Siebers, J. V., Read, P. W., Chen, Q. (2014). Phantomless patient‐specific TomoTherapy QA via delivery performance monitoring and a secondary Monte Carlo dose calculation. Medical physics, 41(10), 101703. https://doi.org/10.1118/1.4894721

Yuan, J., Rong, Y., Chen, Q. (2015). A virtual source model for Monte Carlo simulation of helical tomotherapy. Journal of Applied Clinical Medical Physics, 16(1), 69-85. https://doi.org/10.1120/jacmp.v16i1.4992

Yuan, J., Chen, Q., Brindle, J., Zheng, Y., Lo, S., Sohn, J., Wessels, B. (2015). Investigation of nonuniform dose voxel geometry in Monte Carlo calculations. Technology in Cancer Research & Treatment, 14(4), 419-427. https://doi.org/10.1177/1533034614547459

Chen, Q., Lu, W., Chen, Y., Chen, M., Henderson, D., Sterpin, E. (2012). Validation of GPU based TomoTherapy dose calculation engine. Medical physics, 39(4), 1877-1886. https://doi.org/10.1118/1.3693057

Sterpin, E., Chen, Y., Chen, Q., Lu, W., Mackie, T. R., Vynckier, S. (2011). Monte Carlo‐based simulation of dynamic jaws tomotherapy. Medical physics, 38(9), 5230-5238. https://doi.org/10.1118/1.3626486