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CORRECTION article

Front. Neurol., 29 November 2023
Sec. Applied Neuroimaging
Volume 14 - 2023 | https://doi.org/10.3389/fneur.2023.1334962

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

This article is a correction to:

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

    1. Read original article
\r\nPae Sun Suh&#x;Pae Sun Suh1†Wooseok Jung&#x;Wooseok Jung2†Chong Hyun Suh Chong Hyun Suh1*Jinyoung KimJinyoung Kim2Jio OhJio Oh2Hwon HeoHwon Heo1Woo Hyun ShimWoo Hyun Shim1Jae-Sung LimJae-Sung Lim3Jae-Hong LeeJae-Hong Lee3Ho Sung KimHo Sung Kim1Sang Joon KimSang Joon Kim1
  • 1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
  • 2R&D Center, VUNO, Seoul, Republic of Korea
  • 3Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

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

Suh, P. S., Jung, W., Suh, C. H., Kim, J., Oh, J., Heo, H., Shim, W. H., Lim, J.-S., Lee, J.-H., Kim, H. S., and Kim, S. J. (2023). Front. Neurol. 14:1221892. doi: 10.3389/fneur.2023.1221892

In the published article, there was an error in affiliation 1 and 3. Instead of “1Department of Radiology, Asan Medical Center, Seoul, Republic of Korea; 3Department of Neurology, Asan Medical Center, College of Medicine, University of Ulsan, Ulsan, Republic of Korea”, it should be “1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; 3Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea”.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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

Citation: Suh PS, Jung W, Suh CH, Kim J, Oh J, Heo H, Shim WH, Lim J-S, Lee J-H, Kim HS and Kim SJ (2023) Corrigendum: Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg. Front. Neurol. 14:1334962. doi: 10.3389/fneur.2023.1334962

Received: 08 November 2023; Accepted: 10 November 2023;
Published: 29 November 2023.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2023 Suh, Jung, Suh, Kim, Oh, Heo, Shim, Lim, Lee, Kim and Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Chong Hyun Suh, Y2hvbmdoeXVuc3VoJiN4MDAwNDA7YW1jLnNlb3VsLmty

†These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.