Multi-Task Learning For Thyroid Nodule Segmentation With Thyroid Region Prior

Haifan Gong, Guanqi Chen, Ranran Wang, Xiang Xie, Mingzhi Mao, Yizhou Yu, Fei Chen, Guanbin Li

Abstract: Thyroid nodule segmentation in ultrasound images is a valuable and challenging task, and it is of great significance for the diagnosis of thyroid cancer. Due to the lack of the prior knowledge of thyroid region perception, the inherent low contrast of ultrasound images and the complex appearance changes between different frames of ultrasound video, existing automatic segmentation algorithms for thyroid nodules that directly apply semantic segmentation techniques can easily mistake non-thyroid areas as nodules. In this work, we propose a thyroid region prior guided feature enhancement network (TRFE-Net) for thyroid nodule segmentation. In order to facilitate the development of thyroid nodule segmentation, we have contributed TN3k: an open-access dataset of thyroid nodule images with high-quality nodule masks labeling. Our proposed method is evaluated on TN3k and shows outstanding performance compared with existing state-of the-art algorithms. Source code and data are available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .