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Related Concept Videos

The Thyroid Gland01:23

The Thyroid Gland

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The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...
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Related Experiment Video

Updated: Aug 14, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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FDE-net: Frequency-domain enhancement network using dynamic-scale dilated convolution for thyroid nodule

Hongyu Chen1, Ming-An Yu2, Cheng Chen1

  • 1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.

Computers in Biology and Medicine
|January 11, 2023
PubMed
Summary
This summary is machine-generated.

Accurate thyroid nodule segmentation is crucial for diagnosing potential cancers. FDE-Net, a novel deep learning network, enhances image details and scale adaptability, achieving superior segmentation performance in clinical ultrasound images.

Keywords:
Convolutional neural networkFDE-NetFrequency-domain enhancementImage segmentationThyroid nodule

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Endocrinology

Background:

  • Thyroid nodules are common endocrine system diseases with a nearly 10% malignancy risk.
  • Accurate segmentation of thyroid nodules is vital for effective clinicopathological diagnosis and patient management.

Purpose of the Study:

  • To develop and evaluate FDE-Net, a novel network for accurate and rapid automatic segmentation of thyroid nodules.
  • To improve the robustness and precision of thyroid nodule segmentation in ultrasound images.

Main Methods:

  • FDE-Net combines segmental frequency domain enhancement with a dynamic scale cavity convolutional network.
  • Image omics methods enhance feature images in the frequency domain, reducing noise and improving contour details.
  • A cascade cross-scale attention module fuses multi-receptive field features to address target scale variations.

Main Results:

  • FDE-Net demonstrated a Dice coefficient of 83.54% for thyroid nodule segmentation on 1355 ultrasound images.
  • The proposed method outperformed existing techniques in quantitative evaluations.
  • The network effectively reduced noise and enhanced crucial image details for segmentation.

Conclusions:

  • FDE-Net offers a significant advancement in automatic thyroid nodule segmentation.
  • The network's ability to handle scale variations and enhance image features leads to improved diagnostic accuracy.
  • FDE-Net facilitates accurate and rapid segmentation, supporting clinical decision-making for thyroid nodules.