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CORRECTION article

Front. Neurosci., 20 September 2022
Sec. Brain Imaging Methods
Volume 16 - 2022 | https://doi.org/10.3389/fnins.2022.1034239

Corrigendum: N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation

This article is a correction to:

  1. N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation

    1. Read original article
\nXingqing Nie,Xingqing Nie1,2Xiaogen Zhou,Xiaogen Zhou1,2Tong Tong,, Tong Tong1,2,3*Xingtao Lin,Xingtao Lin1,2Luoyan Wang,Luoyan Wang1,2Haonan Zheng,Haonan Zheng1,2Jing Li,Jing Li1,2Ensheng Xue,Ensheng Xue4,5Shun Chen, Shun Chen4,5*Meijuan Zheng,Meijuan Zheng4,5Cong Chen,Cong Chen4,5Min Du,Min Du1,2
  • 1College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
  • 2Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, China
  • 3Imperial Vision Technology, Fuzhou, China
  • 4Fujian Medical Ultrasound Research Institute, Fuzhou, China
  • 5Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China

A corrigendum on
N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation

by Nie, X., Zhou, X., Tong, T., Lin, X., Wang, L., Zheng, H., Li, J., Xue, E., Chen, S., Zheng, M., Chen, C., and Du, M. (2022). Front. Neurosci. 16:872601. doi: 10.3389/fnins.2022.872601

In the published article, there was an error regarding the affiliations for authors Ensheng Xue, Shun Chen, Meijuan Zheng, Cong Chen. As well as having affiliation(s) 4 they should also be affiliated to “5 Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.”

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 convolutional neural network, medical image segmentation, dilated convolution, multi-scale input layer, thyroid nodule

Citation: Nie X, Zhou X, Tong T, Lin X, Wang L, Zheng H, Li J, Xue E, Chen S, Zheng M, Chen C and Du M (2022) Corrigendum: N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation. Front. Neurosci. 16:1034239. doi: 10.3389/fnins.2022.1034239

Received: 01 September 2022; Accepted: 02 September 2022;
Published: 20 September 2022.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2022 Nie, Zhou, Tong, Lin, Wang, Zheng, Li, Xue, Chen, Zheng, Chen and Du. 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: Tong Tong, ttraveltong@gmail.com; Shun Chen, shunzifjmu@126.com

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.