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of 347
pro vyhledávání: '"Morgan, Howard"'
Early identification of head and neck cancer (HNC) patients who would experience significant anatomical change during radiotherapy (RT) is important to optimize patient clinical benefit and treatment resources. This study aims to assess the feasibili
Externí odkaz:
http://arxiv.org/abs/2405.05674
Autor:
Balagopal, Anjali, Dohopolski, Michael, Kwon, Young Suk, Montalvo, Steven, Morgan, Howard, Bai, Ti, Nguyen, Dan, Liang, Xiao, Zhong, Xinran, Lin, Mu-Han, Desai, Neil, Jiang, Steve
Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to investigate whether dose-sparing to the internal-pudendal-arteries (IP
Externí odkaz:
http://arxiv.org/abs/2302.01493
Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate auto-segmentation to reduce the time cost for physicians to edit contours. However, deep learning (DL)-based direct segmentation of CBCT images is a challenging task, mainly du
Externí odkaz:
http://arxiv.org/abs/2206.03413
Autor:
Liang, Xiao, Chun, Jaehee, Morgan, Howard, Bai, Ti, Nguyen, Dan, Park, Justin C., Jiang, Steve
Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) images. Propagating expert-drawn contours from the pre-treatment planning
Externí odkaz:
http://arxiv.org/abs/2202.03978
Autor:
Ma, Lin, Chi, Weicheng, Morgan, Howard E., Lin, Mu-Han, Chen, Mingli, Sher, David, Moon, Dominic, Vo, Dat T., Avkshtol, Vladimir, Lu, Weiguo, Gu, Xuejun
Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such
Externí odkaz:
http://arxiv.org/abs/2108.08731
Autor:
Bai, Ti, Balagopal, Anjali, Dohopolski, Michael, Morgan, Howard E., McBeth, Rafe, Tan, Jun, Lin, Mu-Han, Sher, David J., Nguyen, Dan, Jiang, Steve
Automatic segmentation of anatomical structures is critical for many medical applications. However, the results are not always clinically acceptable and require tedious manual revision. Here, we present a novel concept called artificial intelligence
Externí odkaz:
http://arxiv.org/abs/2107.13465
Autor:
Balagopal, Anjali, Dohopolski, Michael, Suk Kwon, Young, Montalvo, Steven, Morgan, Howard, Bai, Ti, Nguyen, Dan, Liang, Xiao, Zhong, Xinran, Lin, Mu-Han, Desai, Neil, Jiang, Steve
Publikováno v:
In Physics and Imaging in Radiation Oncology April 2024 30
Autor:
Balagopal, Anjali, Morgan, Howard, Dohopoloski, Michael, Timmerman, Ramsey, Shan, Jie, Heitjan, Daniel F., Liu, Wei, Nguyen, Dan, Hannan, Raquibul, Garant, Aurelie, Desai, Neil, Jiang, Steve
Automatic segmentation of medical images with DL algorithms has proven to be highly successful. With most of these algorithms, inter-observer variation is an acknowledged problem, leading to sub-optimal results. This problem is even more significant
Externí odkaz:
http://arxiv.org/abs/2102.07880
Autor:
Balagopal, Anjali, Nguyen, Dan, Mashayekhi, Maryam, Morgan, Howard, Garant, Aurelie, Desai, Neil, Hannan, Raquibul, Lin, Mu-Han, Jiang, Steve
Inter-observer variation is a significant problem in clinical target volume(CTV) segmentation in postoperative settings, where there is no gross tumor present. In this scenario, the CTV is not an anatomically established structure, but one determined
Externí odkaz:
http://arxiv.org/abs/2102.01006