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
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