An automatic and accurate deep learning-based neuroimaging pipeline for the neonatal brain.

Journal: Pediatric radiology
Published Date: Mar 8, 2023

Abstract

BACKGROUND: Accurate segmentation of neonatal brain tissues and structures is crucial for studying normal development and diagnosing early neurodevelopmental disorders. However, there is a lack of an end-to-end pipeline for automated segmentation and imaging analysis of the normal and abnormal neonatal brain.

Authors

  • Dan Dan Shen
    Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20 Xisi Road, Nantong, Jiangsu, 226001, People's Republic of China.
  • Shan Lei Bao
    Department of Nuclear Medicine, Affiliated Hospital and Medical School of Nantong University, Jiangsu, People's Republic of China.
  • Yan Wang
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Ying Chi Chen
    Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20 Xisi Road, Nantong, Jiangsu, 226001, People's Republic of China.
  • Yu Cheng Zhang
    Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20 Xisi Road, Nantong, Jiangsu, 226001, People's Republic of China.
  • Xing Can Li
    Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20 Xisi Road, Nantong, Jiangsu, 226001, People's Republic of China.
  • Yu Chen Ding
    Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20 Xisi Road, Nantong, Jiangsu, 226001, People's Republic of China.
  • Zhong Zheng Jia
    Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20 Xisi Road, Nantong, Jiangsu, 226001, People's Republic of China. jzz2397@163.com.

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