Corrigendum: Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg

Affiliations

¹ Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
² R&D Center, VUNO, Seoul, Republic of Korea.
³ Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

^# Contributed equally.

Published Erratum

Pae Sun Suh et al. Front Neurol. 2023.

. 2023 Nov 29:14:1334962.

doi: 10.3389/fneur.2023.1334962. eCollection 2023.

¹ Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
² R&D Center, VUNO, Seoul, Republic of Korea.
³ Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

^# Contributed equally.

Abstract

[This corrects the article DOI: 10.3389/fneur.2023.1221892.].

Keywords: artificial intelligence; brain; deep learning; intracranial volume segmentation; neurodegenerative disease.

Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg.
Suh PS, Jung W, Suh CH, Kim J, Oh J, Heo H, Shim WH, Lim JS, Lee JH, Kim HS, Kim SJ. Suh PS, et al. Front Neurol. 2023 Sep 1;14:1221892. doi: 10.3389/fneur.2023.1221892. eCollection 2023. Front Neurol. 2023. PMID: 37719763 Free PMC article.