Deep Learning Contouring of Thoracic Organs At Risk

被引:0
|
作者
Peressutti, D. [1 ]
Aljabar, P. [1 ]
van Soest, J. [2 ]
Lustberg, T. [2 ]
van der Stoep, J. [2 ]
Dekker, A. [2 ]
van Elmpt, W. [2 ]
Gooding, M. [1 ]
机构
[1] Mirada Med Ltd, Sci & Med Technol, Oxford, England
[2] Maastricht Univ, Med Ctr, Dept Radiat Oncol, MAASTRO GROW Sch Oncol Dev Biol, Maastricht, Netherlands
基金
“创新英国”项目;
关键词
D O I
暂无
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
TU-FG-605-
引用
收藏
页码:3159 / 3159
页数:1
相关论文
共 50 条
  • [11] Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy
    Brouwer, Charlotte L.
    Boukerroui, Djamal
    Oliveira, Jorge
    Looney, Padraig
    Steenbakkers, Roel J. H. M.
    Langendijk, Johannes A.
    Both, Stefan
    Gooding, Mark J.
    PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2020, 16 : 54 - 60
  • [12] Temporal changes in IMRT contouring of organs at risk for nasopharyngeal carcinoma: A learning curve?
    Baxi, S. H.
    Chong, V.
    Park, E.
    Chung, H. T.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2008, 72 (01): : S422 - S423
  • [13] AI Model of Using Stratified Deep Learning to Delineate the Organs at Risk (OARs) for Thoracic Radiation Therapy
    Ye, X.
    Guo, D.
    Liu, J.
    Ge, J.
    Yu, H.
    Wang, F.
    Lu, Z.
    Sun, X.
    Yuan, S.
    Zhao, L.
    Jin, X.
    Li, J.
    He, C.
    Zhang, Q.
    Meng, Y.
    Yang, X.
    Liang, J.
    Liu, R.
    Ding, S.
    Zhao, J.
    Li, Z.
    Zhong, W.
    Zhu, B.
    Zhou, S.
    Yuan, T.
    Yan, L.
    Hua, X.
    Lu, L.
    Yan, S.
    Jin, D.
    Kong, S.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2022, 114 (03): : E127 - E127
  • [14] Human factors in the clinical implementation of deep learning-based automated contouring of pelvic organs at risk for MRI-guided radiotherapy
    Abdulkadir, Yasin
    Luximon, Dishane
    Morris, Eric
    Chow, Phillip
    Kishan, Amar U.
    Mikaeilian, Argin
    Lamb, James M.
    MEDICAL PHYSICS, 2023, 50 (10) : 5969 - 5977
  • [15] Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis
    Liu, Peiru
    Sun, Ying
    Zhao, Xinzhuo
    Yan, Ying
    BIOMEDICAL ENGINEERING ONLINE, 2023, 22 (01)
  • [16] Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis
    Peiru Liu
    Ying Sun
    Xinzhuo Zhao
    Ying Yan
    BioMedical Engineering OnLine, 22
  • [17] Clinical Evaluation of Atlas and Deep Learning-Based Automatic Contouring of Multiple Organs at Risk and Clinical Target Volumes for Breast Cancer
    Choi, M.
    Choi, B.
    Chung, S.
    Kim, N.
    Chun, J.
    Kim, Y.
    Chang, J.
    Kim, J.
    MEDICAL PHYSICS, 2020, 47 (06) : E588 - E588
  • [18] Comparison of automatic Segmentation Tools for Contouring Risk Organs
    Cinar, E.
    Friedrich, A. L.
    Zink, K.
    Boettcher, M.
    Engenhart-Cabillic, R.
    Vorwerk, H.
    STRAHLENTHERAPIE UND ONKOLOGIE, 2017, 193 : S124 - S124
  • [19] Hybrid 3D-ResNet Deep Learning Model for Automatic Segmentation of Thoracic Organs at Risk in CT Images
    Qayyum, Abdul
    Ang, Chun Kit
    Sridevi, S.
    Khan, M. K. A. Ahamed
    Hong, Lim Wei
    Mazher, Moona
    Tran Duc Chung
    2020 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING, APPLICATIONS AND MANUFACTURING (ICIEAM), 2020,
  • [20] Evaluating deep learning auto-contouring for lung radiation therapy: A review of accuracy, variability, efficiency and dose, in target volumes and organs at risk
    Moran, Keeva
    Poole, Claire
    Barrett, Sarah
    PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2025, 33