3D Thyroid Segmentation in CT Using Self-attention Convolutional Neural Network

The thyroid gland is a butterfly-shaped organ and belongs to the endocrine system. The abnormality in shape and volume of thyroid can reveal the occurrence of various diseases. Ultrasound (US) imaging is currently the most popular diagnostic tool for diagnosing thyroid diseases. However, most physicians would still make decisions depending on computed tomography (CT) because of its excellent resolution to show more details of the thyroid and its surroundings The thyroid CT imaging before surgery is important because it can assist in determining the anatomical distribution of a lesion and its involvement in adjacent organs or tissues. However, precise segmentation of the thyroid relies heavily on the experience of the physician and is very time-consuming. In this work, we propose to use a 3D deep attention U-Net method to segment the thyroid from CT image automatically. The quantitative evaluation of the segmentation performance of the proposed method, we calculated the Dice similarity coefficient (DSC), sensitivity, specificity, and mean surface distance (MSD) indices between the ground truth and automatic segmentation We demonstrated high accuracy and robustness of the proposed deep-learning-based segmentation method visually and quantitatively. The resultant DSC, precision, and recall were 85% 6%, 86% 5% and 90% 5%, respectively.