Synthetic Sonar Image Simulation with Various Seabed Conditions for Automatic Target Recognition

We propose a novel method to generate underwater object imagery that is acoustically compliant with that generated by side-scan sonar using the Unreal Engine. We describe the process to develop, tune, and generate imagery to provide representative images for use in training automated target recognition (ATR) and machine learning algorithms. The methods provide visual approximations for acoustic effects such as back-scatter noise and acoustic shadow, while allowing fast rendering with C++ actor in UE for maximizing the size of potential ATR training datasets. Additionally, we provide analysis of its utility as a replacement for actual sonar imagery or physics-based sonar data.

Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos

Ultrasound examination is widely used in the clinical diagnosis of thyroid nodules (benign/malignant). However, the accuracy relies heavily on radiologist experience. Although deep learning techniques have been investigated for thyroid nodules recognition. Current solutions are mainly based on static ultrasound images, with limited temporal information used and inconsistent with clinical diagnosis. This paper proposes a novel method for the automated recognition of thyroid nodules through an exhaustive exploration of ultrasound videos and key-frames. We first propose a detection-localization framework to automatically identify the clinical key-frame with a typical nodule in each ultrasound video. Based on the localized key-frame, we develop a key-frame guided video classification model for thyroid nodule recognition. Besides, we introduce a motion attention module to help the network focus on significant frames in an ultrasound video, which is consistent with clinical diagnosis. The proposed thyroid nodule recognition framework is validated on clinically collected ultrasound videos, demonstrating superior performance compared with other state-of-the-art methods.