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Updated: Jun 26, 2026

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

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

Published on: April 21, 2023

Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

Jinlian Ma^1,2, Fa Wu², Tian'an Jiang³

¹State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China.

International Journal of Computer Assisted Radiology and Surgery

|August 2, 2017

Summary

This summary is machine-generated.

This study introduces a deep convolutional neural network (CNN) for automatic thyroid nodule segmentation in ultrasound images, outperforming existing methods. The CNN model offers an efficient and accurate alternative to manual segmentation for clinical applications.

Keywords:

Convolutional neural network Segmentation Thyroid nodule Ultrasound image

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Thyroid nodule segmentation is crucial for diagnosis and clinical index calculation.
Accurate segmentation is challenging due to nodule heterogeneity and background similarity.

Purpose of the Study:

To develop and evaluate a deep convolutional neural network (CNN) for automatic thyroid nodule segmentation in ultrasound images.
To compare the CNN-based method against traditional segmentation techniques.

Main Methods:

A CNN-based approach was developed, treating segmentation as a patch classification task.
A multi-view strategy was implemented to enhance the CNN model's performance.
The method was validated on a dataset of thyroid ultrasound images.

Main Results:

The proposed CNN method demonstrated superior performance compared to existing segmentation approaches.
The model accurately and effectively delineated multiple thyroid nodules.
Quantitative metrics including Dice ratio and true positive rate indicated high accuracy.

Conclusions:

The developed method is fully automatic, requiring no user interaction.
The CNN-based approach is efficient and accurate, offering a viable replacement for manual segmentation.
The study highlights the potential clinical utility of automated thyroid nodule segmentation.