Develop of multi-modal medical image annotation system based on image cloud_West China Medical Journal

Authors：

YANG Hao ^1,2 , ZHANG Rui ^1,2 ,  WANG Miye ^1,2

1. Information center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Engineering Research Center of Medical Information Technology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041,P. R. China;

Corresponding author：

WANG Miye, Email: 9188820@qq.com

Keywords：

Multi-modal; Data annotation; System design; Medical imaging cloud platform

DOI：

10.7507/1002-0179.202012182

Video：

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In the field of artificial intelligence (AI) medical imaging, data annotation is a key factor in all AI development. In the traditional manual annotation process, there are prominent problems such as difficult data acquisition, high manual labor intensity, strong professionalism and low labeling quality. Therefore, an intelligent multimodal medical image annotation system is urgently needed to meet the requirements of labeling. Based on the image cloud, West China Hospital of Sichuan University collected the multimodal image data of hospital and allied hospitals, and designed a multi-modal image annotation system through information technology, which integrated various image processing algorithms and AI models to simplify the image data annotation. With the construction of annotation system, the efficiency of data labeling in the hospitals is improved, which provides necessary data support for the AI image research and related industry construction in the hospital, so as to promote the implementation of artificial intelligence industry related to medical images in the hospital.

Citation： YANG Hao, ZHANG Rui, WANG Miye. Develop of multi-modal medical image annotation system based on image cloud. West China Medical Journal, 2021, 36(9): 1271-1276. doi: 10.7507/1002-0179.202012182 Copy

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16.	解玉琳. 基于数据温度的冷热数据识别机制研究. 杭州: 浙江大学, 2019.

1. 万艳丽, 郭珉江, 马豪, 等. 海量医学影像自动语义标注研究. 中国数字医学, 2015, 10(5): 83-85, 88.
2. 郑光远, 刘峡壁, 韩光辉. 医学影像计算机辅助检测与诊断系统综述. 软件学报, 2018, 29(5): 1471-1514.
3. Alonso O. Challenges with label quality for supervised learning. JDIQ, 2015, 6(1): 1-3.
4. Elizebeth G. AI firms lure academics. Nature, 2016, 532(4): 422-423.
5. 蒋东毅, 吕述望, 罗晓广. VPN的关键技术分析. 计算机工程与应用, 2003, 39(15): 173-177.
6. 高静, 段会川. JSON数据传输效率研究. 计算机工程与设计, 2011, 32(7): 2267-2270.
7. 曾文晔, 方路平, 万铮结. 医学图像处理平台3D Slicer的国际化方法. 计算机系统应用, 2013, 22(7): 7-11.
8. 李星. 数据驱动建模中精度与泛化平衡问题研究. 西安: 西安理工大学, 2020.
9. 张永德, 彭景春, 刘罡, 等. 基于水平集的前列腺磁共振图像分割方法研究. 仪器仪表学报, 2017, 38(2): 416-424.
10. 王植, 贺赛先. 一种基于Canny理论的自适应边缘检测方法. 中国图象图形学报, 2004, 9(8): 957-962.
11. Hegadi RS, Halli SS, Kop A. Segmentation of abnormal region from endoscopic images using intelligent scissors. Int J Comput Appl, 2010: 89-96.
12. 陈哲, 黄巍, 陈昭. 基于Web应用的医学图像半自动标注系统. 计算机应用与软件, 2019, 36(10): 1-5.
13. 陈科, 林江莉. 基于DICOM标准的医学图像数据库的设计. 信息与电脑, 2017(14): 113-115.
14. 王涛. HTTP协议技术浅析. 中国新技术新产品, 2013(22): 14.
15. 齐来军, 周丽娟, 任仲山. 医学影像云存储模型的研究与设计. 计算机应用与软件, 2017, 34(2): 64-70, 122.
16. 解玉琳. 基于数据温度的冷热数据识别机制研究. 杭州: 浙江大学, 2019.

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