Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination

TL;DR

This paper presents a novel deep learning-based system for automatic nodule identification and differentiation in ultrasound videos, aiming to assist clinicians and reduce examination time.

Contribution

It introduces the first application of re-identification techniques in ultrasound imaging, combining feature extraction and real-time clustering for nodule differentiation.

Findings

System achieves satisfactory differentiation of ultrasound videos.

Demonstrates the feasibility of deep learning for ultrasound nodule re-identification.

First attempt to apply re-identification in ultrasonic imaging.

Abstract

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.