Zobrazeno 1 - 10
of 286
pro vyhledávání: '"Lewis, John H"'
Publikováno v:
2021 Medical Physics
Convolutional neural networks have achieved excellent results in automatic medical image segmentation. In this study, we proposed a novel 3D multi-path DenseNet for generating the accurate glioblastoma (GBM) tumor contour from four multi-modal pre-op
Externí odkaz:
http://arxiv.org/abs/2005.04901
Autor:
Fu, Jie, Singhrao, Kamal, Zhong, Xinran, Gao, Yu, Qi, Sharon, Yang, Yingli, Ruan, Dan, Lewis, John H
Publikováno v:
Advances in Radiation Oncology, 2021
We proposed a fully automatic workflow for glioblastoma (GBM) survival prediction using deep learning (DL) methods. 285 glioma (210 GBM, 75 low-grade glioma) patients were included. 163 of the GBM patients had overall survival (OS) data. Every patien
Externí odkaz:
http://arxiv.org/abs/2001.11155
Autor:
Fu, Jie, Singhrao, Kamal, Cao, Minsong, Yu, Victoria, Santhanam, Anand P., Yang, Yingli, Guo, Minghao, Raldow, Ann C., Ruan, Dan, Lewis, John H.
Publikováno v:
2020 Biomed. Phys. Eng. Express
Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-co
Externí odkaz:
http://arxiv.org/abs/1908.04809
To achieve magnetic resonance (MR)-only radiotherapy, a method needs to be employed to estimate a synthetic CT (sCT) for generating electron density maps and patient positioning reference images. We investigated 2D and 3D convolutional neural network
Externí odkaz:
http://arxiv.org/abs/1803.00131
Autor:
Bitterman, Danielle S. *, Selesnick, Philip *, Bredfeldt, Jeremy *, Williams, Christopher L. *, Guthier, Christian *, Huynh, Elizabeth *, Kozono, David E. *, Lewis, John H., Cormack, Robert A. *, Carpenter, Colin M., Mak, Raymond H. *, Atkins, Katelyn M. *
Publikováno v:
In International Journal of Radiation Oncology, Biology, Physics 15 March 2022 112(4):996-1003
Autor:
Williams, Christopher L., Mishra, Pankaj, Seco, Joao, James, Sara St., Mak, Raymond H., Berbeco, Ross I., Lewis, John H.
The XCAT phantom is a realistic 4D digital torso phantom that is widely used in imaging and therapy research. However, lung mass is not conserved between respiratory phases of the phantom, making detailed dosimetric simulations and dose accumulation
Externí odkaz:
http://arxiv.org/abs/1306.4218
Autor:
Li, Ruijiang, Lewis, John H., Jia, Xun, Gu, Xuejun, Folkerts, Michael, Men, Chunhua, Song, William Y., Jiang, Steve B.
Recently we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing pa
Externí odkaz:
http://arxiv.org/abs/1102.1712
Autor:
Li, Ruijiang, Jia, Xun, Lewis, John H., Gu, Xuejun, Folkerts, Michael, Men, Chunhua, Jiang, Steve B.
Purpose: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Methods: Given a set of volumetric images of a patient at N breathing phas
Externí odkaz:
http://arxiv.org/abs/1004.0014
Autor:
O'Connell, Dylan, Shaverdian, Narek, Kishan, Amar U., Thomas, David H., Dou, Tai H., Lewis, John H., Lamb, James M., Cao, Minsong, Tenn, Stephen, Percy, Lee P., Low, Daniel A.
Publikováno v:
In Practical Radiation Oncology May-June 2018 8(3):e175-e183
Akademický článek
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