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The Thyroid Gland

The Thyroid Gland

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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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Published on: April 21, 2023

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

Xingqing Nie^1,2, Xiaogen Zhou^1,2, Tong Tong^1,2,3

¹College of Physics and Information Engineering, Fuzhou University, Fuzhou, China.

Frontiers in Neuroscience

|September 19, 2022

Summary

We introduce N-Net, a novel deep convolutional neural network for segmenting thyroid nodules in ultrasound images. N-Net enhances early disease diagnosis by outperforming existing methods in segmentation accuracy.

Keywords:

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

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer-Aided Diagnosis

Background:

Medical image segmentation is crucial for computer-aided diagnosis (CAD) systems.
Thyroid nodule segmentation in ultrasound images aids early thyroid disease diagnosis.
Deep convolutional neural networks (DCNNs), like U-Net, are widely used for medical image segmentation.

Purpose of the Study:

To propose a novel N-shape dense fully convolutional neural network (N-Net) for enhanced medical image segmentation.
To improve the accuracy and efficiency of thyroid nodule segmentation in ultrasound images.

Main Methods:

The N-Net framework incorporates a multi-scale input layer for rich feature representation.
An attention guidance module filters features and reduces background noise in skip connections.
A stackable dilated convolution (SDC) block captures deep semantic features.

Main Results:

N-Net was evaluated on the TNUI-2021 and DDTI datasets for thyroid nodule segmentation.
The proposed N-Net model demonstrated superior performance compared to several state-of-the-art methods.
Experimental results confirm the effectiveness of N-Net in thyroid nodule segmentation tasks.

Conclusions:

The novel N-Net architecture offers significant improvements for medical image segmentation.
N-Net shows great potential for advancing computer-aided diagnosis in thyroid disease detection.
The proposed components effectively enhance feature representation and semantic understanding.