Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images

Journal article

Han, D., Ibrahim, N., Lu, F., Zhu, Y., Du, H. and AlZoubi, A. 2023. Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images. Ultrasonic Imaging. pp. 1-18. https://doi.org/10.1177/01617346231200804
AuthorsHan, D., Ibrahim, N., Lu, F., Zhu, Y., Du, H. and AlZoubi, A.
Abstract

Thyroid cancer is one of the common types of cancer worldwide, and Ultrasound (US) imaging is a modality normally used for thyroid cancer diagnostics. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR
TIRADS) has been widely adopted to identify and classify US image characteristics for thyroid nodules. This paper presents novel methods for detecting the characteristic descriptors derived from TIRADS. Our methods return descriptions of the nodule margin irregularity, margin smoothness, calcification as well as shape and echogenicity using conventional computer vision and deep learning techniques. We evaluate our methods using datasets of 471 US images of thyroid nodules acquired from US machines of different makes and labeled by multiple radiologists. The proposed methods achieved overall accuracies of 88.00%, 93.18%, and 89.13% in classifying nodule calcification, margin irregularity, and margin smoothness respectively.
Further tests with limited data also show a promising overall accuracy of 90.60% for echogenicity and 100.00% for nodule
shape. This study provides an automated annotation of thyroid nodule characteristics from 2D ultrasound images. The
experimental results showed promising performance of our methods for thyroid nodule analysis. The automatic detection of correct characteristics not only offers supporting evidence for diagnosis, but also generates patient reports rapidly, thereby decreasing the workload of radiologists and enhancing productivity.

Keywordsthyroid cancer; ultrasonography; TIRADS; nodule characteristics; machine learning; computer-aided diagnosis
Year2023
JournalUltrasonic Imaging
Journal citationpp. 1-18
PublisherSAGE Journals
ISSN1096-0910
Digital Object Identifier (DOI)https://doi.org/10.1177/01617346231200804
Web address (URL)https://journals.sagepub.com/doi/abs/10.1177/01617346231200804
https://us.sagepub.com/en-us/nam/journal/ultrasonic-imaging
Accepted author manuscript
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Open
Output statusPublished
Publication dates
Online21 Oct 2023
Publication process dates
Accepted27 Aug 2023
Deposited23 Oct 2023
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License: CC BY-NC-ND 4.0
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