A Survey on Automatic Delineation of Radiotherapy Target Volume based on Machine Learning

被引：0
|
作者：
Tao, Zhenchao [1 ,2 ,3 ]
Lyu, Shengfei [3 ]
机构：
[1] Univ Sci & Technol China, Sch Data Sci, Hefei 230026, Anhui, Peoples R China
[2] Univ Sci & Technol China, Affiliated Hosp USTC 1, Sch Life Sci & Med, Hefei 230031, Peoples R China
[3] Nanyang Technol Univ, Singapore 639798, Singapore
来源：
DATA INTELLIGENCE | 2023年 / 5卷 / 03期
关键词：
Automatic delineation; Machine learning; Radiotherapy target volume; Medical image matching; Cancer; CONVOLUTIONAL NEURAL-NETWORK; ATLAS-BASED SEGMENTATION; IMAGE REGISTRATION; INTEROBSERVER VARIABILITY; RADIATION-THERAPY; AUTO-SEGMENTATION; BRAIN-STEM; CANCER; ORGANS; CT;
D O I：
10.1162/dint_a_00204
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Radiotherapy is one of the main treatment methods for cancer, and the delineation of the radiotherapy target area is the basis and premise of precise treatment. Artificial intelligence technology represented by machine learning has done a lot of research in this area, improving the accuracy and efficiency of target delineation. This article will review the applications and research of machine learning in medical image matching, normal organ delineation and treatment target delineation according to the procudures of doctors to delineate the target volume, and give an outlook on the development prospects.
引用
收藏
页码：841 / 856
页数：16
相关论文
共 50 条
  • [21] Deep-Learning-Based Delineation of the Clinical Target Volume for Glioma Patients
    Soderberg, J.
    Shusharina, N.
    Lofman, F.
    Shih, H.
    Bortfeld, T.
    MEDICAL PHYSICS, 2018, 45 (06) : E219 - E219
  • [22] A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy
    Yang, Jinzhong
    Beadle, Beth M.
    Garden, Adam S.
    Schwartz, David L.
    Aristophanous, Michalis
    MEDICAL PHYSICS, 2015, 42 (09) : 5310 - 5320
  • [23] Development and Validation of a Deep Learning-Based Auto-Delineation of Target Volume and Organs at Risk in Pancreatic Cancer Radiotherapy
    Ren, G.
    Wang, Y.
    Wang, Y.
    Chen, Y.
    Chen, Q.
    Wang, S.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2023, 117 (02): : E482 - E483
  • [24] Automatic Delineation of the Hippocampus Based On Deep Learning Using Multimodal Imaging for Cerebral Radiotherapy
    Zhang, R.
    Liu, Y.
    Fu, C.
    Zhang, J.
    Li, D.
    Chen, C.
    Dai, Z.
    Jia, L.
    Zhang, W.
    MEDICAL PHYSICS, 2022, 49 (06) : E299 - E299
  • [25] Comparison of different target volume delineation strategies based on recurrence patterns in adjuvant radiotherapy for glioblastoma
    Yilmaz, Melek Tugce
    Kahvecioglu, Alper
    Yedekci, Fazli Yagiz
    Yigit, Ecem
    Ciftci, Gokcen Coban
    Kertmen, Neyran
    Zorlu, Faruk
    Yazici, Gozde
    NEURO-ONCOLOGY PRACTICE, 2024, 11 (03) : 275 - 283
  • [26] Proposal for the delineation of the clinical target volume in biliary tract cancer radiotherapy
    Socha, J.
    Michalak, M.
    Wolakiewicz, G.
    Kepka, L.
    RADIOTHERAPY AND ONCOLOGY, 2016, 119 : S334 - S335
  • [27] New target volume delineation and PTV strategies to further personalise radiotherapy
    Bernstein, David
    Taylor, Alexandra
    Nill, Simeon
    Oelfke, Uwe
    PHYSICS IN MEDICINE AND BIOLOGY, 2021, 66 (05):
  • [28] Variability of clinical target volume delineation for definitive radiotherapy in cervix cancer
    Eminowicz, Gemma
    McCormack, Mary
    RADIOTHERAPY AND ONCOLOGY, 2015, 117 (03) : 542 - 547
  • [29] Imaging for Target Volume Delineation in Rectal Cancer Radiotherapy - A Systematic Review
    Gwynne, S.
    Mukherjee, S.
    Webster, R.
    Spezi, E.
    Staffurth, J.
    Coles, B.
    Adams, R.
    CLINICAL ONCOLOGY, 2012, 24 (01) : 52 - 63
  • [30] The dosimetric impact of target volume delineation variation for cervical cancer radiotherapy
    Eminowicz, Gemma
    Rompokos, Vasilis
    Stacey, Christopher
    McCormack, Mary
    RADIOTHERAPY AND ONCOLOGY, 2016, 120 (03) : 493 - 499
12345