no code implementations • 3 Feb 2023 • 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
Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.
no code implementations • 19 Nov 2022 • Anjali Balagopal, Dan Nguyen, Ti Bai, Michael Dohopolski, Mu-Han Lin, Steve Jiang
With adaptation based on only the first three patients, the average DSCs were improved from 78. 6, 71. 9, 63. 0, 52. 2, 46. 3 and 69. 6 to 84. 4, 77. 8, 73. 0, 77. 8, 70. 5, 68. 1, for CTVstyle1, CTVstyle2, and CTVstyle3, Parotidsuperficial, Rectumsuperior, and Rectumposterior, respectively, showing the great potential of the Priorguided DDL network for a fast and effortless adaptation to new practice styles
no code implementations • 28 Jul 2021 • Ti Bai, Anjali Balagopal, Michael Dohopolski, Howard E. Morgan, Rafe McBeth, Jun Tan, Mu-Han Lin, David J. Sher, Dan Nguyen, Steve Jiang
The proposed clinical workflow of AIACR is as follows given an initial contour that requires a clinicians revision, the clinician indicates where a large revision is needed, and a trained deep learning (DL) model takes this input to update the contour.
no code implementations • 15 Jun 2021 • Maryam Mashayekhi, Itzel Ramirez Tapia, Anjali Balagopal, Xinran Zhong, Azar Sadeghnejad Barkousaraie, Rafe McBeth, Mu-Han Lin, Steve Jiang, Dan Nguyen
Typically, the current dose prediction models are limited to small amounts of data and require re-training for a specific site, often leading to suboptimal performance.
no code implementations • 15 Feb 2021 • Anjali Balagopal, Howard Morgan, Michael Dohopoloski, Ramsey Timmerman, Jie Shan, Daniel F. Heitjan, Wei Liu, Dan Nguyen, Raquibul Hannan, Aurelie Garant, Neil Desai, Steve Jiang
A classifier is trained to identify which physician has contoured the CTV from just the contour and corresponding CT scan, to determine if physician styles are consistent and learnable.
no code implementations • 1 Feb 2021 • Anjali Balagopal, Dan Nguyen, Maryam Mashayekhi, Howard Morgan, Aurelie Garant, Neil Desai, Raquibul Hannan, Mu-Han Lin, Steve Jiang
In this study, we analyze the impact that variations in physician style have on dose to organs-at-risk(OAR) by simulating the clinical workflow via deep learning.
no code implementations • 1 Nov 2020 • Dan Nguyen, Azar Sadeghnejad Barkousaraie, Gyanendra Bohara, Anjali Balagopal, Rafe McBeth, Mu-Han Lin, Steve Jiang
We propose to use Monte Carlo dropout (MCDO) and the bootstrap aggregation (bagging) technique on deep learning models to produce uncertainty estimations for radiation therapy dose prediction.
no code implementations • 28 Apr 2020 • Anjali Balagopal, Dan Nguyen, Howard Morgan, Yaochung Weng, Michael Dohopolski, Mu-Han Lin, Azar Sadeghnejad Barkousaraie, Yesenia Gonzalez, Aurelie Garant, Neil Desai, Raquibul Hannan, Steve Jiang
Automating post-operative prostate CTV segmentation with traditional image segmentation methods has been a major challenge.
no code implementations • 31 May 2018 • Anjali Balagopal, Samaneh Kazemifar, Dan Nguyen, Mu-Han Lin, Raquibul Hannan, Amir Owrangi, Steve Jiang
Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning.