Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning-Reference-Cited by-同舟云学术

Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning

Published:2021 Issue: Volume: Page:
ISSN:1347-3182
Container-title:Magnetic Resonance in Medical Sciences
language:en
Short-container-title:magn reson med sci

Author:

Wada Akihiko¹,Saito Yuya¹,Fujita Shohei¹,Irie Ryusuke¹,Akashi Toshiaki¹,Sano Katsuhiro¹,Kato Shinpei¹,Ikenouchi Yutaka¹,Hagiwara Akifumi¹,Sato Kanako¹,Tomizawa Nobuo¹,Hayakawa Yayoi¹,Kikuta Junko¹,Kamagata Koji¹,Suzuki Michimasa¹,Hori Masaaki¹,Nakanishi Atsushi¹,Aoki Shigeki¹

Affiliation:

1. Department of Radiology, Juntendo University School of Medicine

Publisher

Japanese Society for Magnetic Resonance in Medicine

Subject

Radiology, Nuclear Medicine and imaging

Link

https://www.jstage.jst.go.jp/article/mrms/advpub/0/advpub_mp.2021-0068/_pdf

Reference32 articles.

1. 1. Roche AF, Garn SM, Reynolds EL, et al. The first seriatim study of human growth and middle aging. Am J Phys Anthropol 1981; 54:23–24.

2. 2. Dobbing J, Sands J. Quantitative growth and development of human brain. Arch Dis Child 1973; 48:757–767.

3. 3. Squires LA, Krishnamoorthy KS, Natowicz MR. Delayed myelination in infants and young children: radiographic and clinical correlates. J Child Neurol 1995; 10:100–104.