Localization and Risk Stratification of Thyroid Nodules in Ultrasound Images Through Deep Learning

Abstract

ObjectiveDeep learning algorithms have commonly been used for the differential diagnosis between benign and malignant thyroid nodules. In this study, we aimed to develop an integrated system that combines deep learning model and clinical standard thyroid imaging reporting and data system (TI-RADS) for the simultaneous segmentation and risk stratification of thyroid nodules. Methods304 ultrasound images from two independent sites with TI-RADS 4 thyroid nodules were collected. Edge-connection and Criminisi algorithm was used to remove manually-induced markers in ultrasound images. An integrated system based on TI-RADS and mask region-based convolution neural network (Mask R-CNN) was proposed to stratify subclasses of TI-RADS 4 thyroid nodules and to segment thyroid nodules in the ultrasound images. Accuracy and precision-recall curve were used to evaluate stratification performance, dice similarity coefficient (DSC) between the segmentation of Mask R-CNN and the radiologist's contour was used to evaluate segmentation performance of the model. ResultsThe combined approach could significantly enhance the performance of the proposed integrated system. Overall stratification accuracy of TI-RADS 4 thyroid nodules, mean average precision, and mean DSC of the proposed model in the independent test set were 90.79%, 0.8579, and 0.83, respectively. Specifically, stratification accuracy for TI-RADS 4a, 4b and 4c thyroid nodules were 95.83%, 84.21%, and 77.78%. ConclusionAn integrated system combining TI-RADS and deep learning model was developed. The system can provide clinicians with not only diagnostic assistance from TI-RADS, but also accurate segmentation of thyroid nodules, which improves the applicability of the system in clinical practice.