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Updated: Nov 21, 2025

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

A threshold-based method to predict thyroid nodules on scintigraphy scans.

Joseph N Stember¹, Arif Sheikh², Edgar Perez³

¹Memorial Sloan Kettering Cancer Center Department of Radiology, 1275 York Ave, NY, NY 10065, United States of America.

Biomedical Physics & Engineering Express

|January 13, 2021

Summary

This summary is machine-generated.

A new computer-aided detection (CAD) system effectively highlights hot and cold thyroid nodules in Nuclear Medicine imaging. This thresholding approach shows good agreement with expert readers, potentially reducing missed diagnoses.

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Area of Science:

Nuclear Medicine and Radiology
Medical Imaging Analysis
Computational Pathology

Background:

Nuclear Medicine imaging is crucial for evaluating thyroid function and characterizing thyroid nodules.
Hypofunctioning ('cold') nodules have a higher malignancy risk than hyperfunctioning ('hot') nodules.
Subtle density variations in Nuclear Medicine images can lead to missed nodules, necessitating improved detection methods.

Purpose of the Study:

To develop and evaluate a computer-aided detection (CAD) scheme for identifying hot and cold thyroid nodules.
To assess the accuracy of a thresholding-based CAD system in detecting nodules compared to expert readers.

Main Methods:

The proposed CAD approach subdivides thyroid images into regions and applies pixel density cutoffs.
A thresholding technique, similar to that used in PET/CT systems, is employed to highlight suspect regions.
The system was tested on 22 nodules (11 hot, 11 cold) and compared against consensus selections from two expert radiologists.

Main Results:

The CAD system demonstrated good agreement with the consensus diagnoses of expert readers.
All cases showed some overlap between the CAD-highlighted nodules and those identified by experts.
The thresholding approach successfully identified both hot and cold nodules.

Conclusions:

A thresholding-based CAD scheme shows promise for improving the detection of thyroid nodules in Nuclear Medicine.
This system has the potential to reduce false negatives by drawing attention to subtle or overlooked nodules.
Further validation with larger datasets is warranted to confirm the clinical utility of this CAD approach.