Development and Evaluation of Deep Learning-Based Automatic Segmentation Model for Skull Zero TE MRI in Children-Reference-Cited by-同舟云学术

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Development and Evaluation of Deep Learning-Based Automatic Segmentation Model for Skull Zero TE MRI in Children

Published:2023 Issue:1 Volume:27 Page:42
ISSN:2384-1095
Container-title:Investigative Magnetic Resonance Imaging
language:en
Short-container-title:Investig Magn Reson Imaging

Author:

Seo Yun Seok¹^ORCID,Choi Young Hun¹²^ORCID,Lee Joon Sung³^ORCID,Lee Seul Bi¹²^ORCID,Cho Yeon Jin¹²^ORCID,Lee Seunghyun¹²^ORCID,Shin Su-Mi⁴^ORCID,Cheon Jung-Eun¹²⁵^ORCID

Affiliation:

1. Department of Radiology, Seoul National University Children’s Hospital, Seoul, Korea.

2. Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.

3. GE Healthcare Korea, Seoul, Korea.

4. Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea.

5. Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.

Funder

Korean Society of Magnetic Resonance in Medicine (KSMRM)

Seoul National University Hospital

Publisher

XMLink

Subject

General Computer Science

Link

https://i-mri.org/pdf/10.13104/imri.2022.1114

Reference20 articles.

1. Application of three-dimensional computed tomography in craniofacial clinical practice and research

2. Risk of Ionizing Radiation Exposure to Children: A Subject Review

3. ZTE imaging with enhanced flip angle using modulated excitation

4. MRI with zero echo time: Hard versus sweep pulse excitation

5. Zero TEMR bone imaging in the head

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