Abstract
In recent years, the incidence of thyroid cancer has increased dramatically, resulting in an increased demand for early thyroid nodule examination. Ultrasound (US) imaging is the modality most frequently used to image thyroid nodules; However, the low image resolution, speckle noise, and high variability make it difficult to utilize traditional image processing techniques. Recent advances in deep learning (DL) have increased research into the automated processing of thyroid US images. We review three main image processing tasks for thyroid nodule analysis: classification, segmentation, and detection. We discuss the advantages and limitations of the recently proposed DL techniques as well as the data availability and algorithmic efficacy. In addition, we investigate the remaining obstacles and future potential for automated analysis of thyroid US images.
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