Artificial intelligence-based automatic nidus segmentation of cerebral arteriovenous malformation on time-of-flight magnetic resonance angiography.

Journal: European journal of radiology
Published Date: Jun 13, 2024

Abstract

OBJECTIVE: Accurate nidus segmentation and quantification have long been challenging but important tasks in the clinical management of Cerebral Arteriovenous Malformation (CAVM). However, there are still dilemmas in nidus segmentation, such as difficulty defining the demarcation of the nidus, observer-dependent variation and time consumption. The aim of this study isto develop an artificial intelligence model to automatically segment the nidus on Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) images.

Authors

  • Mengqi Dong
    Department of Neurosurgery, Guangdong General Hospital, Institute of Neuroscience, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, China.
  • Sishi Xiang
    China International Neuroscience Institute (China-INI), Beijing, China.
  • Tao Hong
    ICF International, 2635 Meridian Pkwy #200, Durham, NC 27713, United States.
  • Chunxue Wu
    Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China. Electronic address: wuchunxue130@hotmail.com.
  • Jiaxing Yu
    Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; China International Neuroscience Institute, Beijing, China. Electronic address: 15311435081@163.com.
  • Kun Yang
    Department of Bone and Joint Surgery, Affiliated Hospital of Southwest Medical University, Luzhou Sichuan, 646000, P.R.China.
  • Wanxin Yang
    Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; China International Neuroscience Institute, Beijing, China. Electronic address: yang_wx98@163.com.
  • Xiangyu Li
  • Jian Ren
    State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China. Electronic address: renjian@sysucc.org.cn.
  • Hailan Jin
    Department of R&D, UnionStrong (Beijing) Technology Co.Ltd, Beijing, China.
  • Ye Li
    Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Science, Haikou 571010, People's Republic of China; Key Laboratory of Monitoring and Control of Tropical Agricultural and Forest Invasive Alien Pests, Ministry of Agriculture, Haikou 571010, People's Republic of China.
  • Guilin Li
    Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; China International Neuroscience Institute, Beijing, China. Electronic address: lgl723@sina.com.
  • Ming Ye
    Department of Scientific Computing, Florida State University, Tallahassee, FL 32306, USA.
  • Jie Lu
    Department of Endocrinology and Metabolism, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
  • Hongqi Zhang
    China International Neuroscience Institute (China-INI), Beijing, China xwzhanghq@163.com qinlan@unionstrongtech.com.

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