Automatic nodule identification and differentiation in ultrasound videos
  to facilitate per-nodule examination
Siyuan Jiang Yan Ding Yuling Wang Lei Xu Wenli Dai Wanru Chang Jianfeng Zhang Jie Yu Jianqiao Zhou Chunquan Zhang Ping Liang Dexing Kong
Ultrasound is a vital diagnostic technique in health screening, with the
advantages of non-invasive, cost-effective, and radiation free, and therefore
is widely applied in the diagnosis of nodules. However, it relies heavily on
the expertise and clinical experience of the sonographer. In ultrasound images,
a single nodule might present heterogeneous appearances in different
cross-sectional views which makes it hard to perform per-nodule examination.
Sonographers usually discriminate different nodules by examining the nodule
features and the surrounding structures like gland and duct, which is
cumbersome and time-consuming. To address this problem, we collected hundreds
of breast ultrasound videos and built a nodule reidentification system that
consists of two parts: an extractor based on the deep learning model that can
extract feature vectors from the input video clips and a real-time clustering
algorithm that automatically groups feature vectors by nodules. The system
obtains satisfactory results and exhibits the capability to differentiate
ultrasound videos. As far as we know, it's the first attempt to apply
re-identification technique in the ultrasonic field.