A Prospective Observational Study of Clinical Acceptability of Deep Learning Model for the Automated Segmentation of Organs at Risk for Head and Neck Radiotherapy Treatment Planning

被引：0

作者：

Lucido, J. ^{[1
]}

DeWees, T. A. ^{[2
]}

Leavitt, T. ^{[3
]}

Anand, A. ^{[4
]}

Beltran, C. ^{[5
]}

Brooke, M. ^{[6
]}

Buroker, J. ^{[7
]}

Foote, R. L. ^{[8
]}

Foss, O. R. ^{[1
]}

Hughes, C. O. ^{[6
]}

Hunzeker, A. ^{[5
]}

Laack, N. N., II ^{[5
]}

Lenz, T. ^{[9
]}

Morigami, M. ^{[6
]}

Moseley, D. J. ^{[5
]}

Patel, Y. ^{[6
]}

Tryggestad, E. J. ^{[5
]}

Wilson, M. Z. ^{[6
]}

Zverovitch, A. ^{[6
]}

Patel, S. H. ^{[10
]}

机构：

[1] Mayo Clin, Rochester, MN USA

[2] Mayo Clin, Scottsdale, AZ USA

[3] Mayo Clin, Dept Quantitat Hlth Sci, Scottsdale, AZ USA

[4] Mayo Clin, Dept Radiat Oncol, Phoenix, AZ USA

[5] Mayo Clin, Dept Radiat Oncol, Rochester, MN USA

[6] Google Hlth, Mountain View, CA USA

[7] Mayo Clin Rochester, Rochester, MN USA

[8] Mayo Clin Rochester, Dept Radiat Oncol, Rochester, MN USA

[9] Mayo Clin, Rochester Campus, Rochester, MN USA

[10] Mayo Clin Arizona, Dept Radiat Oncol, Phoenix, AZ USA

来源：

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS | 2022年 / 114卷 / 03期

关键词：

D O I：

暂无

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

2267

引用

页码：E121 / E121

页数：1

共 50 条

[21] Automated confidence estimation in deep learning auto-segmentation for brain organs at risk on MRI for radiotherapy
Alzahrani, Nouf M.
Henry, Ann M.
Al-Qaisieh, Bashar M.
Murray, Louise J.
Nix, Michael G.
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2024, 25 (12):
[22] Evaluation of Intra-Observer Variation for Deep Learning Generated Head and Neck Organs at Risk Segmentation
Ge, J.
Ye, X.
Guo, D.
Song, Y.
Hua, X.
Lu, L.
Lin, C. Y.
Jin, D.
Ho, T. Y.
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2022, 114 (03): : E477 - E477
[23] Dosimetry Validation Study for Automated Head and Neck Cancer Organs at Risk Segmentation Using Stratified Learning and Neural Architecture Search
Ge, J.
Guo, D.
Ye, X.
Song, Y.
Hua, X.
Lu, L.
Lin, C. Y.
Jin, D.
Ho, T. Y.
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2022, 114 (03): : E583 - E583
[24] Deep learning segmentation of organs-at-risk with integration into clinical workflow for pediatric brain radiotherapy
Mekki, Lina
Acharya, Sahaja
Ladra, Matthew
Lee, Junghoon
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2024, 25 (03):
[25] Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system
Costea, Madalina
Zlate, Alexandra
Durand, Morgane
Baudier, Thomas
Gregoire, Vincent
Sarrut, David
Biston, Marie-Claude
RADIOTHERAPY AND ONCOLOGY, 2022, 177 : 61 - 70
[26] Automated Segmentation of Head-And-Neck CT Images for Radiotherapy Treatment Planning Via Multi-Atlas Machine Learning (MAML)
Ren, X.
Sharp, G.
Gao, H.
MEDICAL PHYSICS, 2016, 43 (06) : 3321 - 3321
[27] Development of a Novel Deep Learning Algorithm for Autosegmentation of Clinical Tumor Volume and Organs at Risk in Head and Neck Radiation Therapy Planning
Ibragimov, B.
Pernus, F.
Strojan, P.
Xing, L.
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2016, 96 (02): : S226 - S226
[28] Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study
Johnson, Casey L.
Press, Robert H.
Simone, Charles B.
Shen, Brian
Tsai, Pingfang
Hu, Lei
Yu, Francis
Apinorasethkul, Chavanon
Ackerman, Christopher
Zhai, Huifang
Lin, Haibo
Huang, Sheng
FRONTIERS IN ONCOLOGY, 2024, 14
[29] Quantitative evaluation of deep learning contouring of head and neck organs at risk
Bakker, H.
Peressutti, D.
Aljabar, P.
Van Dijk, L. V.
Van den Bosch, L.
Gooding, M.
Brouwer, C. L.
RADIOTHERAPY AND ONCOLOGY, 2018, 127 : S217 - S218
[30] Benefits of deep learning for delineation of organs at risk in head and neck cancer
Van der Veen, J.
Willems, S.
Robben, D.
Crijns, W.
Maes, F.
Nuyts, S.
RADIOTHERAPY AND ONCOLOGY, 2019, 133 : S371 - S371

← 1 2 3 4 5 →