Abstract
Abstract Ultrasound (US) imaging is used as a preliminary diagnostic tool for the detection, risk-stratification and classification of thyroid nodules. In order to perform the risk stratification of nodules in US images physicians first need to effectively detect the nodules. This process is affected due to the presence of inter-observer and intra-observer variability and subjectivity. Computer Aided Diagnostic tools prove to be a step in the right direction towards reducing the issue of subjectivity and observer variability. Several segmentation techniques have been proposed, from these Deep Learning techniques have yielded promising results. This work presents a comparison between four state of the art (SOTA) Deep Learning segmentation algorithms (UNet, SUMNet, ResUNet and Attention UNet). Each network was trained on the same dataset and the results are compared using performance metrics such as accuracy, dice coefficient and Intersection over Union (IoU) to determine the most effective in terms of thyroid nodule segmentation in US images. It was found that ResUNet performed the best with an accuracy, dice coefficient and IoU of 89.2%, 0.857, 0.767. The aim is to use the trained algorithm in the development of a Computer Aided Diagnostic system for the detection, riskstratification and classification of thyroid nodules using US images to reduce subjectivity and observer variability
Highlights
Thyroid nodules are solid or cystic lumps in the thyroid gland, which can either be benign or malignant, and they are one of the most commonly diagnosed nodular lesions in the adult population [1]
Ultrasound (US) imaging is used as a preliminary diagnostic tool for the detection, riskstratification and classification of thyroid nodules
This paper presents a comparison between four state-of-theart (SOTA) algorithms developed for the purpose of image segmentation
Summary
Thyroid nodules are solid or cystic lumps in the thyroid gland, which can either be benign or malignant, and they are one of the most commonly diagnosed nodular lesions in the adult population [1]. Ultrasound (US) imaging is used as a preliminary diagnostic tool for the detection, riskstratification and classification of thyroid nodules. It is used because of its availability, affordability and lack of ionizing radiation. In order to efficiently provide a risk-stratification and classification of a thyroid nodule, a physician first needs to detect the nodule. The current detection process is highly subjective in nature and there exists a high rate of interobserver and intra-observer variability. This stems from the varied experience levels and visual perceptions of the physicians when it comes to detecting the nodules in US thyroid images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
