Self-supervised learning enhanced ultrasound video thyroid nodule tracking

Thyroid nodules are found in 19% to 67% of individuals who are screened for thyroid cancer using ultrasonography. The large number of individuals examined for thyroid cancer is placing significant stress on radiologists and the healthcare system. Video-scale detection requires more training data than frame-scale detection. To alleviate the need for large-scale labeled datasets, a patch-scale self-supervised pre-training model was trained on unlabeled data to extract patch-distinguishing features, which are crucial for object detection. The pre-trained model was transferred to the video-based model to improve the performance of nodule detection. Experimental test results on 22 ultrasound videos containing 47 nodules show that the performance of our proposed method is 0.523 for mAP@50 and 0.430 for HOTA. The proposed method can process at 23 fps, which can meet the requirements of real-time tracking in screening scenarios.