Automated Segmentation of Thyroid Nodule, Gland, and Cystic Components From Ultrasound Images Using Deep Learning.

Viksit Kumar,John M Knudsen,Duane D Meixner,Mostafa Fatemi,Jeremy Webb,Adriana Gregory,Matthew Callstrom,Azra Alizad

doi:10.1109/access.2020.2982390

Viksit Kumar, John M Knudsen + Show 6 more

Open Access

https://doi.org/10.1109/access.2020.2982390

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Abstract

Sonographic features associated with margins, shape, size, and volume of thyroid nodules are used to assess their risk of malignancy. Automatically segmenting nodules from normal thyroid gland would enable an automated estimation of these features. A novel multi-output convolutional neural network algorithm with dilated convolutional layers is presented to segment thyroid nodules, cystic components inside the nodules, and normal thyroid gland from clinical ultrasound B-mode scans. A prospective study was conducted, collecting data from 234 patients undergoing a thyroid ultrasound exam before biopsy. The training and validation sets encompassed 188 patients total; the testing set consisted of 48 patients. The algorithm effectively segmented thyroid anatomy into nodules, normal gland, and cystic components. The algorithm achieved a mean Dice coefficient of 0.76, a mean true positive fraction of 0.90, and a mean false positive fraction of 1.61×10-6. The values are on par with a conventional seeded algorithm. The proposed algorithm eliminates the need for a seed in the segmentation process, thus automatically detecting and segmenting the thyroid nodules and cystic components. The detection rate for thyroid nodules and cystic components was 82% and 44%, respectively. The inference time per image, per fold was 107ms. The mean error in volume estimation of thyroid nodules for five select cases was 7.47%. The algorithm can be used for detection, segmentation, size estimation, volume estimation, and generating thyroid maps for thyroid nodules. The algorithm has applications in point of care, mobile health monitoring, improving workflow, reducing localization time, and assisting sonographers with limited expertise.

Highlights

The increase in incidence of thyroid cancer is faster than any other cancer at 4.5% per year over the last 10 years [1]
The box plots for Dice coefficient, true positive fraction (TPF), and false positive fraction (FPF) versus suspicion level using the multi-prong convolutional neural network (MPCNN) and distance regularized level set (DRLS) algorithms are shown in Fig. 3,4, and 5 respectively
The box plots for Dice coefficient, TPF, and FPF versus pathology using the MPCNN and DRLS algorithms are shown in Fig. 6, 7, and 8, respectively

Summary

Introduction

The increase in incidence of thyroid cancer is faster than any other cancer at 4.5% per year over the last 10 years [1]. In 2018, an estimated 53,990 new thyroid cancer cases were diagnosed in the United States alone, and an estimated 2,060 people died due to thyroid cancer [2]. The United States Preventive Services Task Force recommends against screening, including neck palpation and ultrasound. Due to the lack of a screening process, thyroid nodules are found incidentally by palpation or diagnostic imaging modalities like ultrasonography, computed tomography, magnetic resonance imaging, or positron emission tomography. Ultrasonography is the commonly used diagnostic tool for thyroid cancer as it is inexpensive and readily available. Besides differentiating between solid nodules and those consisting of cystic components, ultrasonography features are related to the pathology of the nodule.

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