Deep Learning-based Hierarchical Brain Segmentation with Preliminary Analysis of the Repeatability and Reproducibility-Reference-Cited by-同舟云学术

Deep Learning-based Hierarchical Brain Segmentation with Preliminary Analysis of the Repeatability and Reproducibility

Published:2024 Issue: Volume: Page:
ISSN:1347-3182
Container-title:Magnetic Resonance in Medical Sciences
language:en
Short-container-title:MRMS

Author:

Goto Masami¹,Kamagata Koji²,Andica Christina²,Takabayashi Kaito²,Uchida Wataru²,Goto Tsubasa³,Yuzawa Takuya³,Kitamura Yoshiro³,Hatano Taku⁴,Hattori Nobutaka⁴,Aoki Shigeki²,Sakamoto Hajime¹,Sakano Yasuaki¹,Kyogoku Shinsuke¹,Daida Hiroyuki¹,

Affiliation:

1. Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan

2. Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan

3. Medical System Research & Development Center, FUJIFILM Corporation, Tokyo, Japan

4. Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan

Funder

National Institutes of Health

U.S. Department of Defense

Publisher

Japanese Society for Magnetic Resonance in Medicine

Link

https://www.jstage.jst.go.jp/article/mrms/advpub/0/advpub_mp.2023-0124/_pdf

Reference44 articles.

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3. 3. Goto M, Suzuki M, Mizukami S, et al. Repeatability of brain volume measurements made with the atlas-based method from T1-weighted images acquired using a 0.4 Tesla low field MR scanner. Magn Reson Med Sci 2016; 15: 365–370.

4. 4. Aljabar P, Heckemann RA, Hammers A, Hajnal JV, Rueckert D. Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. Neuroimage 2009; 46: 726–738.

5. 5. Corona V, Lellmann J, Nestor P, Schonlieb CB, Acosta-Cabronero J. A multi-contrast MRI approach to thalamus segmentation. Hum Brain Mapp 2020; 41: 2104–2120.