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

Xingqing Nie; Xiaogen Zhou; Tong Tong; Xingtao Lin; Luoyan Wang; Haonan Zheng; Jing Li; Ensheng Xue; Shun Chen; Meijuan Zheng; Cong Chen; Min Du

doi:10.3389/fnins.2022.1034239

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

Front Neurosci. 2022 Sep 20:16:1034239. doi: 10.3389/fnins.2022.1034239. eCollection 2022.

Authors

Xingqing Nie^{1

2}, Xiaogen Zhou^{1

2}, Tong Tong^{1

2

3}, Xingtao Lin^{1

2}, Luoyan Wang^{1

2}, Haonan Zheng^{1

2}, Jing Li^{1

2}, Ensheng Xue^{4

5}, Shun Chen^{4

5}, Meijuan Zheng^{4

5}, Cong Chen^{4

5}, Min Du^{1

2}

Affiliations

¹ College of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
² Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, China.
³ Imperial Vision Technology, Fuzhou, China.
⁴ Fujian Medical Ultrasound Research Institute, Fuzhou, China.
⁵ Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.

Abstract

[This corrects the article DOI: 10.3389/fnins.2022.872601.].

Keywords: deep convolutional neural network; dilated convolution; medical image segmentation; multi-scale input layer; thyroid nodule.

Publication types

Published Erratum